Journal of Pathology Informatics Journal of Pathology Informatics
Contact us | Home | Login   |  Users Online: 56  Print this pageEmail this pageSmall font sizeDefault font sizeIncrease font size 


This article has been cited by
1
Eduardo Romero Castro,Germán Corredor,Cheng Lu,Anant Madabhushi,Xiangxue Wang,Vamsidhar Velcheti,Metin N. Gurcan,John E. Tomaszewski
.2018;()26
[DOI]
2
Aditya Sriram,Shivam Kalra,H.R. Tizhoosh
.2019;()1
[DOI]
3Artificial intelligence as the next step towards precision pathology
B. Acs,M. Rantalainen,J. Hartman
Journal of Internal Medicine.2020;()1
[DOI]
4Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists
Yun Liu,Timo Kohlberger,Mohammad Norouzi,George E. Dahl,Jenny L. Smith,Arash Mohtashamian,Niels Olson,Lily H. Peng,Jason D. Hipp,Martin C. Stumpe
Archives of Pathology & Laboratory Medicine.2019;143(7)859
[DOI]
5Blinded Visual Scoring of Images Using the Freely-available Software Blinder
Steven Cothren,Joel Meyer,Jessica Hartman
BIO-PROTOCOL.2018;8(23)859
[DOI]
6A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue
Jeffrey J. Nirschl,Andrew Janowczyk,Eliot G. Peyster,Renee Frank,Kenneth B. Margulies,Michael D. Feldman,Anant Madabhushi,Alison Marsden
PLOS ONE.2018;13(4)e0192726
[DOI]
7A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue
Xiangxue Wang,Vamsidhar Velcheti,Pranjal Vaidya,Kaustav Bera,Anant Madabhushi,Arjun Khunger,Pradnya Patil,Humberto Choi,Metin N. Gurcan,John E. Tomaszewski
PLOS ONE.2018;13(4)21
[DOI]
8A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue
Benjamin Chidester,That-Vinh Ton,Minh-Triet Tran,Jian Ma,Minh N. Do
PLOS ONE.2019;13(4)1097
[DOI]
9Automatic labeling of molecular biomarkers of immunohistochemistry images using fully convolutional networks
Fahime Sheikhzadeh,Rabab K. Ward,Dirk van Niekerk,Martial Guillaud,Christophe Egles
PLOS ONE.2018;13(1)e0190783
[DOI]
10Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma
Hiroaki Miyoshi,Kensaku Sato,Yoshinori Kabeya,Sho Yonezawa,Hiroki Nakano,Yusuke Takeuchi,Issei Ozawa,Shoichi Higo,Eriko Yanagida,Kyohei Yamada,Kei Kohno,Takuya Furuta,Hiroko Muta,Mai Takeuchi,Yuya Sasaki,Takuro Yoshimura,Kotaro Matsuda,Reiji Muto,Mayuko Moritsubo,Kanako Inoue,Takaharu Suzuki,Hiroaki Sekinaga,Koichi Ohshima
Laboratory Investigation.2020;13(1)e0190783
[DOI]
11Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images
Can Taylan Sari,Cigdem Gunduz-Demir
IEEE Transactions on Medical Imaging.2019;38(5)1139
[DOI]
12Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images
Zhihua Liu,Nadiya Abdukeyim,Chuanbo Yan
IEEE Transactions on Medical Imaging.2019;38(5)1280
[DOI]
13Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
Mohamed Amgad,Elisabeth Specht Stovgaard,Eva Balslev,Jeppe Thagaard,Weijie Chen,Sarah Dudgeon,Ashish Sharma,Jennifer K. Kerner,Carsten Denkert,Yinyin Yuan,Khalid AbdulJabbar,Stephan Wienert,Peter Savas,Leonie Voorwerk,Andrew H. Beck,Anant Madabhushi,Johan Hartman,Manu M. Sebastian,Hugo M. Horlings,Jan Hudecek,Francesco Ciompi,David A. Moore,Rajendra Singh,Elvire Roblin,Marcelo Luiz Balancin,Marie-Christine Mathieu,Jochen K. Lennerz,Pawan Kirtani,I-Chun Chen,Jeremy P. Braybrooke,Giancarlo Pruneri,Sandra Demaria,Sylvia Adams,Stuart J. Schnitt,Sunil R. Lakhani,Federico Rojo,Laura Comerma,Sunil S. Badve,Mehrnoush Khojasteh,W. Fraser Symmans,Christos Sotiriou,Paula Gonzalez-Ericsson,Katherine L. Pogue-Geile,Rim S. Kim,David L. Rimm,Giuseppe Viale,Stephen M. Hewitt,John M. S. Bartlett,Frédérique Penault-Llorca,Shom Goel,Huang-Chun Lien,Sibylle Loibl,Zuzana Kos,Sherene Loi,Matthew G. Hanna,Stefan Michiels,Marleen Kok,Torsten O. Nielsen,Alexander J. Lazar,Zsuzsanna Bago-Horvath,Loes F. S. Kooreman,Jeroen A. W. M. van der Laak,Joel Saltz,Brandon D. Gallas,Uday Kurkure,Michael Barnes,Roberto Salgado,Lee A. D. Cooper
npj Breast Cancer.2020;6(1)1280
[DOI]
14Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
Kavya Ravichandran,Nathaniel Braman,Andrew Janowczyk,Anant Madabhushi,Kensaku Mori,Nicholas Petrick
npj Breast Cancer.2018;6(1)11
[DOI]
15Applications of deep learning for the analysis of medical data
Hyun-Jong Jang,Kyung-Ok Cho
Archives of Pharmacal Research.2019;42(6)492
[DOI]
16Advanced Morphologic Analysis for Diagnosing Allograft Rejection
Eliot G. Peyster,Anant Madabhushi,Kenneth B. Margulies
Transplantation.2018;102(8)1230
[DOI]
17Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
Andrew Janowczyk,Anant Madabhushi
Journal of Pathology Informatics.2016;7(1)29
[DOI]
18Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings
Haojia Li,Jon Whitney,Kaustav Bera,Hannah Gilmore,Mangesh A. Thorat,Sunil Badve,Anant Madabhushi
Breast Cancer Research.2019;21(1)29
[DOI]
19Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology
Francesco Ponzio,Gianvito Urgese,Elisa Ficarra,Santa Di Cataldo
Electronics.2019;8(3)256
[DOI]
20Deep feature transfer learning for trusted and automated malware signature generation in private cloud environments
Daniel Nahmias,Aviad Cohen,Nir Nissim,Yuval Elovici
Neural Networks.2020;124(3)243
[DOI]
21Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology
Hanadi El El Achi,Joseph D. Khoury
Cancers.2020;12(4)797
[DOI]
22Deep learning nuclei detection: A simple approach can deliver state-of-the-art results
Henning Höfener,André Homeyer,Nick Weiss,Jesper Molin,Claes F. Lundström,Horst K. Hahn
Computerized Medical Imaging and Graphics.2018;70(4)43
[DOI]
23A novel model for malaria prediction based on ensemble algorithms
Mengyang Wang,Hui Wang,Jiao Wang,Hongwei Liu,Rui Lu,Tongqing Duan,Xiaowen Gong,Siyuan Feng,Yuanyuan Liu,Zhuang Cui,Changping Li,Jun Ma,Pawel Plawiak
PLOS ONE.2019;14(12)e0226910
[DOI]
24A novel model for malaria prediction based on ensemble algorithms
Francesco Ponzio,Enrico Macii,Elisa Ficarra,Santa Di Cataldo
PLOS ONE.2019;1024(12)114
[DOI]
25Transfer learning for classification of cardiovascular tissues in histological images
Claudia Mazo,Jose Bernal,Maria Trujillo,Enrique Alegre
Computer Methods and Programs in Biomedicine.2018;165(12)69
[DOI]
26Transfer learning for classification of cardiovascular tissues in histological images
Elnaz Mohammadi,Mahdi Orooji
Computer Methods and Programs in Biomedicine.2018;165(12)1
[DOI]
27Cell Image Classification: A Comparative Overview
Mohammad Shifat-E-Rabbi,Xuwang Yin,Cailey E. Fitzgerald,Gustavo K. Rohde
Cytometry Part A.2020;97(4)347
[DOI]
28Next generation pathology: artificial intelligence enhances histopathology practice
Balazs Acs,Johan Hartman
The Journal of Pathology.2020;250(1)7
[DOI]
29Review of the current state of digital image analysis in breast pathology
Martin C. Chang,Miralem Mrkonjic
The Breast Journal.2020;250(1)7
[DOI]
30Automated Renal Cancer Grading Using Nuclear Pleomorphic Patterns
Daniel Aitor Holdbrook,Malay Singh,Yukti Choudhury,Emarene Mationg Kalaw,Valerie Koh,Hui Shan Tan,Ravindran Kanesvaran,Puay Hoon Tan,John Yuen Shyi Peng,Min-Han Tan,Hwee Kuan Lee
JCO Clinical Cancer Informatics.2018;250(2)1
[DOI]
31Environmental properties of cells improve machine learning-based phenotype recognition accuracy
Timea Toth,Tamas Balassa,Norbert Bara,Ferenc Kovacs,Andras Kriston,Csaba Molnar,Lajos Haracska,Farkas Sukosd,Peter Horvath
Scientific Reports.2018;8(1)1
[DOI]
32Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings
Gregory Penzias,Asha Singanamalli,Robin Elliott,Jay Gollamudi,Natalie Shih,Michael Feldman,Phillip D. Stricker,Warick Delprado,Sarita Tiwari,Maret Böhm,Anne-Maree Haynes,Lee Ponsky,Pingfu Fu,Pallavi Tiwari,Satish Viswanath,Anant Madabhushi,Aamir Ahmad
PLOS ONE.2018;13(8)e0200730
[DOI]
33Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings
Artem Pimkin,Gleb Makarchuk,Vladimir Kondratenko,Maxim Pisov,Egor Krivov,Mikhail Belyaev
PLOS ONE.2018;10882(8)877
[DOI]
34Feasibility of fully automated classification of whole slide images based on deep learning
Kyung-Ok Cho,Sung Hak Lee,Hyun-Jong Jang
The Korean Journal of Physiology & Pharmacology.2020;24(1)89
[DOI]
35Intelligence artificielle et radiothérapie : quelles bases et quelles perspectives ?
A. Burgun
Cancer/Radiothérapie.2019;23(8)913
[DOI]
36Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks
Xipeng Pan,Dengxian Yang,Lingqiao Li,Zhenbing Liu,Huihua Yang,Zhiwei Cao,Yubei He,Zhen Ma,Yiyi Chen
World Wide Web.2018;21(6)1721
[DOI]
37Deep learning based tissue analysis predicts outcome in colorectal cancer
Dmitrii Bychkov,Nina Linder,Riku Turkki,Stig Nordling,Panu E. Kovanen,Clare Verrill,Margarita Walliander,Mikael Lundin,Caj Haglund,Johan Lundin
Scientific Reports.2018;8(1)1721
[DOI]
38Automatic Nuclear Segmentation Using Multiscale Radial Line Scanning With Dynamic Programming
Hongming Xu,Cheng Lu,Richard Berendt,Naresh Jha,Mrinal Mandal
IEEE Transactions on Biomedical Engineering.2017;64(10)2475
[DOI]
39Automatic Nuclear Segmentation Using Multiscale Radial Line Scanning With Dynamic Programming
Jiamei Sun,Alexander Binder
IEEE Transactions on Biomedical Engineering.2017;64(10)43
[DOI]
40A new era: artificial intelligence and machine learning in prostate cancer
S. Larry Goldenberg,Guy Nir,Septimiu E. Salcudean
Nature Reviews Urology.2019;16(7)391
[DOI]
41Open access image repositories: high-quality data to enable machine learning research
F. Prior,J. Almeida,P. Kathiravelu,T. Kurc,K. Smith,T.J. Fitzgerald,J. Saltz
Clinical Radiology.2020;75(1)7
[DOI]
42A contemporary review of machine learning in otolaryngology–head and neck surgery
Matthew G. Crowson,Jonathan Ranisau,Antoine Eskander,Aaron Babier,Bin Xu,Russel R. Kahmke,Joseph M. Chen,Timothy C. Y. Chan
The Laryngoscope.2020;130(1)45
[DOI]
43Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care
Ugljesa Djuric,Gelareh Zadeh,Kenneth Aldape,Phedias Diamandis
npj Precision Oncology.2017;1(1)45
[DOI]
44Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype
Heather D. Couture,Lindsay A. Williams,Joseph Geradts,Sarah J. Nyante,Ebonee N. Butler,J. S. Marron,Charles M. Perou,Melissa A. Troester,Marc Niethammer
npj Breast Cancer.2018;4(1)45
[DOI]
45Deep Learning Makes Its Way to the Clinical Laboratory
Ronald Jackups
Clinical Chemistry.2017;63(12)1790
[DOI]
46A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome
Nathan Ing,Fangjin Huang,Andrew Conley,Sungyong You,Zhaoxuan Ma,Sergey Klimov,Chisato Ohe,Xiaopu Yuan,Mahul B. Amin,Robert Figlin,Arkadiusz Gertych,Beatrice S. Knudsen
Scientific Reports.2017;7(1)1790
[DOI]
47Intelligence artificielle : quel avenir en anatomie pathologique ?
Ryad Zemouri,Christine Devalland,Séverine Valmary-Degano,Noureddine Zerhouni
Annales de Pathologie.2019;39(2)119
[DOI]
48Intelligence artificielle : quel avenir en anatomie pathologique ?
Bhagirathi Halalli,Aziz Makandar
Annales de Pathologie.2019;1036(2)106
[DOI]
49Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence
Shivam Kalra,H. R. Tizhoosh,Sultaan Shah,Charles Choi,Savvas Damaskinos,Amir Safarpoor,Sobhan Shafiei,Morteza Babaie,Phedias Diamandis,Clinton J. V. Campbell,Liron Pantanowitz
npj Digital Medicine.2020;3(1)106
[DOI]
50Automated Classification for Visual-Only Postmortem Inspection of Porcine Pathology
Stephen McKenna,Telmo Amaral,Ilias Kyriazakis
IEEE Transactions on Automation Science and Engineering.2020;17(2)1005
[DOI]
51Automated Classification for Visual-Only Postmortem Inspection of Porcine Pathology
Scotty Kwok
IEEE Transactions on Automation Science and Engineering.2018;10882(2)931
[DOI]
52Emergence of “Big Data” and Its Potential and Current Limitations in Medical Imaging
Martin J. Yaffe
Seminars in Nuclear Medicine.2019;49(2)94
[DOI]
53Emergence of “Big Data” and Its Potential and Current Limitations in Medical Imaging
Thomas Lampert,Odyssee Merveille,Jessica Schmitz,Germain Forestier,Friedrich Feuerhake,Cedric Wemmert
Seminars in Nuclear Medicine.2019;49(2)905
[DOI]
54Emergence of “Big Data” and Its Potential and Current Limitations in Medical Imaging
Chaoyang Yan,Jun Xu,Jiawei Xie,Chengfei Cai,Haoda Lu
Seminars in Nuclear Medicine.2020;49(2)254
[DOI]
55Deep neural network based histological scoring of lung fibrosis and inflammation in the mouse model system
Fabian Heinemann,Gerald Birk,Tanja Schoenberger,Birgit Stierstorfer,Robert Hurst
PLOS ONE.2018;13(8)e0202708
[DOI]
56A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival
Mustafa I. Jaber,Bing Song,Clive Taylor,Charles J. Vaske,Stephen C. Benz,Shahrooz Rabizadeh,Patrick Soon-Shiong,Christopher W. Szeto
Breast Cancer Research.2020;22(1)e0202708
[DOI]
57A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival
Vaishnavi Subramanian,Benjamin Chidester,Jian Ma,Minh N. Do
Breast Cancer Research.2018;22(1)805
[DOI]
58A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival
Sarang Mahajan,Urmil Shah,Rucha Tambe,Mohit Agrawal,Bhushan Garware
Breast Cancer Research.2019;22(1)1
[DOI]
59A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival
Upeka V. Somaratne,Kok Wai Wong,Jeremy Parry,Ferdous Sohel,Xuequn Wang,Hamid Laga
Breast Cancer Research.2019;22(1)1
[DOI]
60The Use of Deep Learning to Predict Stroke Patient Mortality
Songhee Cheon,Jungyoon Kim,Jihye Lim
International Journal of Environmental Research and Public Health.2019;16(11)1876
[DOI]
61Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques
Xiaohong W. Gao,Yu Qian
Molecular Pharmaceutics.2018;15(10)4326
[DOI]
62Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)
Hassaan Majeed,Tan Huu Nguyen,Mikhail Eugene Kandel,Andre Kajdacsy-Balla,Gabriel Popescu
Scientific Reports.2018;8(1)4326
[DOI]
63Applications of machine learning in drug discovery and development
Jessica Vamathevan,Dominic Clark,Paul Czodrowski,Ian Dunham,Edgardo Ferran,George Lee,Bin Li,Anant Madabhushi,Parantu Shah,Michaela Spitzer,Shanrong Zhao
Nature Reviews Drug Discovery.2019;18(6)463
[DOI]
64Applications of machine learning in drug discovery and development
Angel A. Cruz Roa,Anant Madabhushi,Fabian Cano,Eduardo Romero,Natasha Lepore,Jorge Brieva
Nature Reviews Drug Discovery.2018;18(6)39
[DOI]
65Applications of machine learning in drug discovery and development
Tahsin Kurc,Ashish Sharma,Rajarsi Gupta,Le Hou,Han Le,Shahira Abousamra,Erich Bremer,Ryan Birmingham,Tammy DiPrima,Nan Li,Feiqiao Wang,Joseph Balsamo,Whitney Bremer,Dimitris Samaras,Joel Saltz
Nature Reviews Drug Discovery.2020;11993(6)371
[DOI]
66Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology Images
Ruqayya Awan,Korsuk Sirinukunwattana,David Epstein,Samuel Jefferyes,Uvais Qidwai,Zia Aftab,Imaad Mujeeb,David Snead,Nasir Rajpoot
Scientific Reports.2017;7(1)371
[DOI]
67Computer-aided diagnosis in the era of deep learning
Heang-Ping Chan,Lubomir M. Hadjiiski,Ravi K. Samala
Medical Physics.2020;47(5)371
[DOI]
68Computer-aided diagnosis in the era of deep learning
Bingzhe Wu,Shiwan Zhao,Guangyu Sun,Xiaolu Zhang,Zhong Su,Caihong Zeng,Zhihong Liu
Medical Physics.2019;47(5)2094
[DOI]
69Computer-aided diagnosis in the era of deep learning
Xiaohong W. Gao,Yu Qian,Barjor Gimi,Andrzej Krol
Medical Physics.2018;47(5)93
[DOI]
70Learning to detect lymphocytes in immunohistochemistry with deep learning
Zaneta Swiderska-Chadaj,Hans Pinckaers,Mart van Rijthoven,Maschenka Balkenhol,Margarita Melnikova,Oscar Geessink,Quirine Manson,Mark Sherman,Antonio Polonia,Jeremy Parry,Mustapha Abubakar,Geert Litjens,Jeroen van der Laak,Francesco Ciompi
Medical Image Analysis.2019;58(5)101547
[DOI]
71MuDeRN: Multi-category classification of breast histopathological image using deep residual networks
Ziba Gandomkar,Patrick C. Brennan,Claudia Mello-Thoms
Artificial Intelligence in Medicine.2018;88(5)14
[DOI]
72MuDeRN: Multi-category classification of breast histopathological image using deep residual networks
Kun Fan,Shibo Wen,Zhuofu Deng
Artificial Intelligence in Medicine.2019;145(5)137
[DOI]
73Staining Invariant Features for Improving Generalization of Deep Convolutional Neural Networks in Computational Pathology
Sebastian Otálora,Manfredo Atzori,Vincent Andrearczyk,Amjad Khan,Henning Müller
Frontiers in Bioengineering and Biotechnology.2019;7(5)137
[DOI]
74Systems biology primer: the basic methods and approaches
Walter Kolch,Dirk Fey,Colm J. Ryan,Iman Tavassoly,Joseph Goldfarb,Ravi Iyengar
Essays in Biochemistry.2018;62(4)487
[DOI]
75Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review
Henning Muller,Devrim Unay
IEEE Transactions on Multimedia.2017;19(9)2093
[DOI]
76Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review
Hui Qu,Zhennan Yan,Gregory M. Riedlinger,Subhajyoti De,Dimitris N. Metaxas
IEEE Transactions on Multimedia.2019;11764(9)378
[DOI]
77Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer
Michel E. Vandenberghe,Marietta L. J. Scott,Paul W. Scorer,Magnus Söderberg,Denis Balcerzak,Craig Barker
Scientific Reports.2017;7(1)378
[DOI]
78Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer
Cecília Lantos,Steven M. Kornblau,Amina A. Qutub
Scientific Reports.2018;7(1)378
[DOI]
79Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer
Aman Rana,Gregory Yauney,Lawrence C. Wong,Otkrist Gupta,Ali Muftu,Pratik Shah
Scientific Reports.2017;7(1)144
[DOI]
80Association of Pathological Fibrosis With Renal Survival Using Deep Neural Networks
Vijaya B. Kolachalama,Priyamvada Singh,Christopher Q. Lin,Dan Mun,Mostafa E. Belghasem,Joel M. Henderson,Jean M. Francis,David J. Salant,Vipul C. Chitalia
Kidney International Reports.2018;3(2)464
[DOI]
81COUNTERPOINT: Is ICD-10 Diagnosis Coding Important in the Era of Big Data? No
David M. Liebovitz,John Fahrenbach
Chest.2018;153(5)1095
[DOI]
82Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks
Muyi Sun,Wei Zhou,Xingqun Qi,Guanhong Zhang,Leonard Girnita,Stefan Seregard,Hans Grossniklaus,Zeyi Yao,Xiaoguang Zhou,Gustav Stĺlhammar
Cancers.2019;11(10)1579
[DOI]
83High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection
Angel Cruz-Roa,Hannah Gilmore,Ajay Basavanhally,Michael Feldman,Shridar Ganesan,Natalie Shih,John Tomaszewski,Anant Madabhushi,Fabio González,Yuanquan Wang
PLOS ONE.2018;13(5)e0196828
[DOI]
84Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data
Alexander Effland,Erich Kobler,Anne Brandenburg,Teresa Klatzer,Leonie Neuhäuser,Michael Hölzel,Jennifer Landsberg,Thomas Pock,Martin Rumpf
International Journal of Computer Assisted Radiology and Surgery.2019;14(4)587
[DOI]
85Classification of Benign and Malignant Breast Cancer using Supervised Machine Learning Algorithms Based on Image and Numeric Datasets
Ratula Ray,Azian Azamimi Abdullah,Debasish Kumar Mallick,Satya Ranjan Dash
Journal of Physics: Conference Series.2019;1372(4)012062
[DOI]
86Classification of Benign and Malignant Breast Cancer using Supervised Machine Learning Algorithms Based on Image and Numeric Datasets
Erwan Zerhouni,David Lanyi,Matheus Viana,Maria Gabrani
Journal of Physics: Conference Series.2017;1372(4)924
[DOI]
87Role of deep learning in infant brain MRI analysis
Mahmoud Mostapha,Martin Styner
Magnetic Resonance Imaging.2019;64(4)171
[DOI]
88Artificial intelligence in digital breast pathology: Techniques and applications
Asmaa Ibrahim,Paul Gamble,Ronnachai Jaroensri,Mohammed M. Abdelsamea,Craig H. Mermel,Po-Hsuan Cameron Chen,Emad A. Rakha
The Breast.2020;49(4)267
[DOI]
89An Ensemble Approach for Classification of Breast Histopathology Images
P. Dhivya,S. Vasuki
IETE Journal of Research.2019;49(4)1
[DOI]
90Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology
Oliver C. Turner,Famke Aeffner,Dinesh S. Bangari,Wanda High,Brian Knight,Tom Forest,Brieuc Cossic,Lauren E. Himmel,Daniel G. Rudmann,Bhupinder Bawa,Anantharaman Muthuswamy,Olulanu H. Aina,Elijah F. Edmondson,Chandrassegar Saravanan,Danielle L. Brown,Tobias Sing,Manu M. Sebastian
Toxicologic Pathology.2020;48(2)277
[DOI]
91Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology
Srishti Gautam,Arnav Bhavsar,Anil K. Sao,Harinarayan K.K.,Metin N. Gurcan,John E. Tomaszewski
Toxicologic Pathology.2018;48(2)32
[DOI]
92Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology
Santiago López-Tapia,Cristobal Olivencia,José Aneiros-Fernández,Nicolás Pérez de la Blanca
Toxicologic Pathology.2019;11435(2)144
[DOI]
93Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology
Adon Phillips,Iris Teo,Jochen Lang
Toxicologic Pathology.2019;11435(2)2738
[DOI]
94A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology
Neeraj Kumar,Ruchika Verma,Sanuj Sharma,Surabhi Bhargava,Abhishek Vahadane,Amit Sethi
IEEE Transactions on Medical Imaging.2017;36(7)1550
[DOI]
95A Review of Intrinsic Optical Imaging Serial Blockface Histology (ICI-SBH) for Whole Rodent Brain Imaging
Joël Lefebvre,Patrick Delafontaine-Martel,Frédéric Lesage
Photonics.2019;6(2)66
[DOI]
96Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes
Thomas J S Durant,Eben M Olson,Wade L Schulz,Richard Torres
Clinical Chemistry.2017;63(12)1847
[DOI]
97Deep Learning With Sampling in Colon Cancer Histology
Mary Shapcott,Katherine J. Hewitt,Nasir Rajpoot
Frontiers in Bioengineering and Biotechnology.2019;7(12)1847
[DOI]
98Deep Learning With Sampling in Colon Cancer Histology
Nick Weiss,Henning Kost,André Homeyer
Frontiers in Bioengineering and Biotechnology.2018;10882(12)727
[DOI]
99Deep-learning approaches for Gleason grading of prostate biopsies
Anant Madabhushi,Michael D Feldman,Patrick Leo
The Lancet Oncology.2020;21(2)187
[DOI]
100Deep-learning approaches for Gleason grading of prostate biopsies
Lyndon Chan,Mahdi Hosseini,Corwyn Rowsell,Konstantinos Plataniotis,Savvas Damaskinos
The Lancet Oncology.2019;21(2)10661
[DOI]
101Machine Learning Methods for Histopathological Image Analysis
Daisuke Komura,Shumpei Ishikawa
Computational and Structural Biotechnology Journal.2018;16(2)34
[DOI]
102Robust nuclei segmentation in histopathology using ASPPU-Net and boundary refinement
Tao Wan,Lei Zhao,Hongxiang Feng,Deyu Li,Chao Tong,Zengchang Qin
Neurocomputing.2020;16(2)34
[DOI]
103Automating the Paris System for urine cytopathology—A hybrid deep-learning and morphometric approach
Louis J. Vaickus,Arief A. Suriawinata,Jason W. Wei,Xiaoying Liu
Cancer Cytopathology.2019;127(2)98
[DOI]
104Automating the Paris System for urine cytopathology—A hybrid deep-learning and morphometric approach
Alberto Corvo,Marc A. van Driel,Michel A. Westenberg
Cancer Cytopathology.2017;127(2)77
[DOI]
105Panoptic View of Prognostic Models for Personalized Breast Cancer Management
Geetanjali Saini,Karuna Mittal,Padmashree Rida,Emiel A. M. Janssen,Keerthi Gogineni,Ritu Aneja
Cancers.2019;11(9)1325
[DOI]
106Panoptic View of Prognostic Models for Personalized Breast Cancer Management
Nabila Shawki,M. Golam Shadin,Tarek Elseify,Luke Jakielaszek,Tunde Farkas,Yuri Persidsky,Nirag Jhala,Iyad Obeid,Joseph Picone
Cancers.2020;11(9)69
[DOI]
107Panoptic View of Prognostic Models for Personalized Breast Cancer Management
Ziba Gandomkar,Patrick C. Brennan,Claudia Mello-Thoms,Elizabeth A. Krupinski
Cancers.2018;11(9)54
[DOI]
108Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning
Tomas Iesmantas,Agne Paulauskaite-Taraseviciene,Kristina Sutiene
Applied Sciences.2020;10(2)615
[DOI]
109Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning
Chen Wang,Ji Bao,Hong Bu
Applied Sciences.2017;10(2)2154
[DOI]
110Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software
Andres Moon,Geoffrey H. Smith,Jun Kong,Thomas E. Rogers,Carla L. Ellis,Alton B. “Brad” Farris
Virchows Archiv.2018;472(2)259
[DOI]
111Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software
Sahil Chelaramani,Manish Gupta,Vipul Agarwal,Prashant Gupta,Ranya Habash
Virchows Archiv.2020;12047(2)734
[DOI]
112Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software
Francisco Perdigon Romero,An Tang,Samuel Kadoury
Virchows Archiv.2019;12047(2)1092
[DOI]
113Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload
Julianna D. Ianni,Rajath E. Soans,Sivaramakrishnan Sankarapandian,Ramachandra Vikas Chamarthi,Devi Ayyagari,Thomas G. Olsen,Michael J. Bonham,Coleman C. Stavish,Kiran Motaparthi,Clay J. Cockerell,Theresa A. Feeser,Jason B. Lee
Scientific Reports.2020;10(1)1092
[DOI]
114Improved small blob detection in 3D images using jointly constrained deep learning and Hessian analysis
Yanzhe Xu,Teresa Wu,Fei Gao,Jennifer R. Charlton,Kevin M. Bennett
Scientific Reports.2020;10(1)1092
[DOI]
115MitosisNet: End-to-End Mitotic Cell Detection by Multi-Task Learning
Md Zahangir Alom,Theus Aspiras,Tarek M. Taha,Tj Bowen,Vijayan K. Asari
IEEE Access.2020;8(1)68695
[DOI]
116Impact of pre-analytical variables on deep learning accuracy in histopathology
Andrew D Jones,John Paul Graff,Morgan Darrow,Alexander Borowsky,Kristin A Olson,Regina Gandour-Edwards,Ananya Datta Mitra,Dongguang Wei,Guofeng Gao,Blythe Durbin-Johnson,Hooman H Rashidi
Histopathology.2019;75(1)39
[DOI]
117Impact of pre-analytical variables on deep learning accuracy in histopathology
Anna Korzynska,Jakub Zak,Krzysztof Siemion,Lukasz Roszkowiak,Dorota Pijanowska
Histopathology.2020;1033(1)72
[DOI]
118Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring
Michelle A. Wood-Trageser,Andrew J. Lesniak,Anthony J. Demetris
Transplantation.2019;103(7)1306
[DOI]
119Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Kaustav Bera,Kurt A. Schalper,David L. Rimm,Vamsidhar Velcheti,Anant Madabhushi
Nature Reviews Clinical Oncology.2019;16(11)703
[DOI]
120Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning
Ziang Pei,Shuangliang Cao,Lijun Lu,Wufan Chen
Computational and Mathematical Methods in Medicine.2019;2019(11)1
[DOI]
121Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning
Oscar Jimenez-del-Toro,Sebastian Otálora,Mats Andersson,Kristian Eurén,Martin Hedlund,Mikael Rousson,Henning Müller,Manfredo Atzori
Computational and Mathematical Methods in Medicine.2017;2019(11)281
[DOI]
122The Impact of Artificial Intelligence on the Labor Market
Michael Webb
SSRN Electronic Journal.2019;2019(11)281
[DOI]
123The Impact of Artificial Intelligence on the Labor Market
Rucha Tambe,Sarang Mahajan,Urmil Shah,Mohit Agrawal,Bhushan Garware
SSRN Electronic Journal.2019;2019(11)143
[DOI]
124Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images
Massimo Salvi,Filippo Molinari
BioMedical Engineering OnLine.2018;17(1)143
[DOI]
125Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features
Germán Corredor,Jon Whitney,Viviana Arias,Anant Madabhushi,Eduardo Romero
Journal of Medical Imaging.2017;4(2)021105
[DOI]
126Fluorescence-based quantification of nucleocytoplasmic transport
Joshua B. Kelley,Bryce M. Paschal
Methods.2019;157(2)106
[DOI]
127Fluorescence-based quantification of nucleocytoplasmic transport
Yoshinori Kabeya,Toshiya Iwamori,Sho Yonezawa,Yusuke Takeuchi,Hiroki Nakano,Yuhe Nagisa,Mariko Okubo,Michio Inoue,Reitaro Tokumasu,Issei Ozawa,Atsushi Takano,Ichizo Nishino
Methods.2019;157(2)1850
[DOI]
128Deep Learning in the Biomedical Applications: Recent and Future Status
Ryad Zemouri,Noureddine Zerhouni,Daniel Racoceanu
Applied Sciences.2019;9(8)1526
[DOI]
129Deep Learning in the Biomedical Applications: Recent and Future Status
Bruno Korbar,Andrea M. Olofson,Allen P. Miraflor,Catherine M. Nicka,Matthew A. Suriawinata,Lorenzo Torresani,Arief A. Suriawinata,Saeed Hassanpour
Applied Sciences.2017;9(8)821
[DOI]
130Deep Learning in the Biomedical Applications: Recent and Future Status
Sebastian Otálora,Manfredo Atzori,Vincent Andrearczyk,Henning Müller
Applied Sciences.2018;11039(8)148
[DOI]
131Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images
Lingqiao Li,Xipeng Pan,Huihua Yang,Zhenbing Liu,Yubei He,Zhongming Li,Yongxian Fan,Zhiwei Cao,Longhao Zhang
Multimedia Tools and Applications.2018;11039(8)148
[DOI]
132Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment
Shazia Akbar,Mohammad Peikari,Sherine Salama,Azadeh Yazdan Panah,Sharon Nofech-Mozes,Anne L. Martel
Scientific Reports.2019;9(1)148
[DOI]
133Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images
Yoshimasa Kawazoe,Kiminori Shimamoto,Ryohei Yamaguchi,Yukako Shintani-Domoto,Hiroshi Uozaki,Masashi Fukayama,Kazuhiko Ohe
Journal of Imaging.2018;4(7)91
[DOI]
134Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images
Heang-Ping Chan,Ravi K. Samala,Lubomir M. Hadjiiski,Chuan Zhou
Journal of Imaging.2020;1213(7)3
[DOI]
135Computer aided tool for automatic detection and delineation of nucleus from oral histopathology images for OSCC screening
Dev Kumar Das,Subhranil Koley,Surajit Bose,Asok Kumar Maiti,Bhaskar Mitra,Gopeswar Mukherjee,Pranab Kumar Dutta
Applied Soft Computing.2019;83(7)105642
[DOI]
136Gated multimodal networks
John Arevalo,Thamar Solorio,Manuel Montes-y-Gómez,Fabio A. González
Neural Computing and Applications.2020;83(7)105642
[DOI]
137Gated multimodal networks
Tuan D. Pham,Chuanwen Fan,Hong Zhang,Xiao-Feng Sun
Neural Computing and Applications.2019;83(7)1
[DOI]
138Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis
Sai Chandra Kosaraju,Jie Hao,Hyun Min Koh,Mingon Kang
Methods.2020;83(7)1
[DOI]
139Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis
Lipeng Xie,Chunming Li
Methods.2018;83(7)129
[DOI]
140Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network
Yu Wang,Yating Chen,Ningning Yang,Longfei Zheng,Nilanjan Dey,Amira S. Ashour,V. Rajinikanth,Joăo Manuel R.S. Tavares,Fuqian Shi
Applied Soft Computing.2019;74(7)40
[DOI]
141Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network
Vaishnavi Subramanian,Weizhao Tang,Benjamin Chidester,Jian Ma,Minh N. Do
Applied Soft Computing.2018;11071(7)245
[DOI]
142Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network
Kamal Jnawali,Bhargava Chinni,Vikram Dogra,Navalgund Rao,Horst K. Hahn,Kensaku Mori
Applied Soft Computing.2019;11071(7)140
[DOI]
143Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network
Loris Nanni,Stefano Ghidoni,Sheryl Brahnam,Shaoxiong Liu,Ling Zhang
Applied Soft Computing.2020;186(7)117
[DOI]
144Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network
Chen Wang,Hong Bu,Ji Bao,Chunming Li
Applied Soft Computing.2017;186(7)865
[DOI]
145Segmentation of Glomeruli Within Trichrome Images Using Deep Learning
Shruti Kannan,Laura A. Morgan,Benjamin Liang,McKenzie G. Cheung,Christopher Q. Lin,Dan Mun,Ralph G. Nader,Mostafa E. Belghasem,Joel M. Henderson,Jean M. Francis,Vipul C. Chitalia,Vijaya B. Kolachalama
Kidney International Reports.2019;4(7)955
[DOI]
146Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches
Agnieszka Barbara Szczotka,Dzhoshkun Ismail Shakir,Daniele Ravě,Matthew J. Clarkson,Stephen P. Pereira,Tom Vercauteren
International Journal of Computer Assisted Radiology and Surgery.2020;4(7)955
[DOI]
147Impact of image analysis and artificial intelligence in thyroid pathology, with particular reference to cytological aspects
Ilaria Girolami,Stefano Marletta,Liron Pantanowitz,Evelin Torresani,Claudio Ghimenton,Mattia Barbareschi,Aldo Scarpa,Matteo Brunelli,Valeria Barresi,Pierpaolo Trimboli,Albino Eccher
Cytopathology.2020;4(7)955
[DOI]
148Impact of image analysis and artificial intelligence in thyroid pathology, with particular reference to cytological aspects
Zaneta Swiderska-Chadaj,Tomasz Markiewicz,Bartlomiej Grala,Malgorzata Lorent,Arkadiusz Gertych
Cytopathology.2017;723(7)448
[DOI]
149Quaternion Grassmann average network for learning representation of histopathological image
Jun Shi,Xiao Zheng,Jinjie Wu,Bangming Gong,Qi Zhang,Shihui Ying
Pattern Recognition.2019;89(7)67
[DOI]
150Quaternion Grassmann average network for learning representation of histopathological image
Shamima Nasrin,Md Zahangir Alom,Ranga Burada,Tarek M. Taha,Vijayan K. Asari
Pattern Recognition.2019;89(7)345
[DOI]
151Quaternion Grassmann average network for learning representation of histopathological image
Sajid Javed,Muhammad Moazam Fraz,David Epstein,David Snead,Nasir M. Rajpoot
Pattern Recognition.2018;11039(7)120
[DOI]
152Quantification of hepatic steatosis in histologic images by deep learning method
Fan Yang,Xianyuan Jia,Pinggui Lei,Yan He,Yining Xiang,Jun Jiao,Shi Zhou,Wei Qian,Qinghong Duan
Journal of X-Ray Science and Technology.2020;27(6)1033
[DOI]
153Quantification of hepatic steatosis in histologic images by deep learning method
Nicholas C. Cullen,Brian B. Avants
Journal of X-Ray Science and Technology.2018;136(6)13
[DOI]
154Quantification of hepatic steatosis in histologic images by deep learning method
Guofeng Lv,Ke Wen,Zheng Wu,Xu Jin,Hong An,Jie He
Journal of X-Ray Science and Technology.2019;136(6)357
[DOI]
155Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Gabriele Campanella,Matthew G. Hanna,Luke Geneslaw,Allen Miraflor,Vitor Werneck Krauss Silva,Klaus J. Busam,Edi Brogi,Victor E. Reuter,David S. Klimstra,Thomas J. Fuchs
Nature Medicine.2019;25(8)1301
[DOI]
156Performance of an artificial intelligence algorithm for reporting urine cytopathology
Adit B. Sanghvi,Erastus Z. Allen,Keith M. Callenberg,Liron Pantanowitz
Cancer Cytopathology.2019;127(10)658
[DOI]
157Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools
Dina Sikpa,Jérémie P. Fouquet,Réjean Lebel,Phedias Diamandis,Maxime Richer,Martin Lepage
Scientific Reports.2019;9(1)658
[DOI]
158Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools
Yang Yu,Jiahao Wang,Ha Eun Chun,Yumeng Xu,Eliza Li Shan Fong,Aileen Wee,Hanry Yu
Scientific Reports.2020;9(1)658
[DOI]
159Glomerulus Classification and Detection Based on Convolutional Neural Networks
Jaime Gallego,Anibal Pedraza,Samuel Lopez,Georg Steiner,Lucia Gonzalez,Arvydas Laurinavicius,Gloria Bueno
Journal of Imaging.2018;4(1)20
[DOI]
160An End-to-end System for Automatic Characterization of Iba1 Immunopositive Microglia in Whole Slide Imaging
Alexander D. Kyriazis,Shahriar Noroozizadeh,Amir Refaee,Woongcheol Choi,Lap-Tak Chu,Asma Bashir,Wai Hang Cheng,Rachel Zhao,Dhananjay R. Namjoshi,Septimiu E. Salcudean,Cheryl L. Wellington,Guy Nir
Neuroinformatics.2019;17(3)373
[DOI]
161An End-to-end System for Automatic Characterization of Iba1 Immunopositive Microglia in Whole Slide Imaging
Deniz Sayin Mercadier,Beril Besbinar,Pascal Frossard
Neuroinformatics.2019;17(3)1020
[DOI]
162An End-to-end System for Automatic Characterization of Iba1 Immunopositive Microglia in Whole Slide Imaging
Tuan D. Pham
Neuroinformatics.2017;10262(3)524
[DOI]
163Quantitative chromogenic immunohistochemical image analysis in cellprofiler software
V. Tollemar,N. Tudzarovski,E. Boberg,A. Törnqvist Andrén,A. Al-Adili,K. Le Blanc,K. Garming Legert,M. Bottai,G. Warfvinge,R.V. Sugars
Cytometry Part A.2018;93(10)1051
[DOI]
164Quantitative chromogenic immunohistochemical image analysis in cellprofiler software
Dorota Oszutowska–Mazurek,Oktawian Knap
Cytometry Part A.2017;573(10)466
[DOI]
165Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding
Shuli Cheng,Liejun Wang,Anyu Du
IEEE Access.2019;573(10)1
[DOI]
166Gland segmentation in prostate histopathological images
Malay Singh,Emarene Mationg Kalaw,Danilo Medina Giron,Kian-Tai Chong,Chew Lim Tan,Hwee Kuan Lee
Journal of Medical Imaging.2017;4(2)027501
[DOI]
167Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density
Pejman Rasti,Ali Ahmad,Salma Samiei,Etienne Belin,David Rousseau
Remote Sensing.2019;11(3)249
[DOI]
168Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
Charlotte Syrykh,Arnaud Abreu,Nadia Amara,Aurore Siegfried,Véronique Maisongrosse,François X. Frenois,Laurent Martin,Cédric Rossi,Camille Laurent,Pierre Brousset
npj Digital Medicine.2020;3(1)249
[DOI]
169Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
Sébastien Besson,Roger Leigh,Melissa Linkert,Chris Allan,Jean-Marie Burel,Mark Carroll,David Gault,Riad Gozim,Simon Li,Dominik Lindner,Josh Moore,Will Moore,Petr Walczysko,Frances Wong,Jason R. Swedlow
npj Digital Medicine.2019;11435(1)3
[DOI]
170Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images
Jun Xu,Lei Gong,Guanhao Wang,Cheng Lu,Hannah Gilmore,Shaoting Zhang,Anant Madabhushi
Journal of Medical Imaging.2019;6(01)1
[DOI]
171Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images
Lipeng Xie,Jin Qi,Lili Pan,Samad Wali
Neurocomputing.2020;376(01)166
[DOI]
172Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images
Sadiq Alinsaif,Jochen Lang
Neurocomputing.2019;376(01)1424
[DOI]
173A Lightweight Convolutional Neural Network Based on Visual Attention for SAR Image Target Classification
Jiaqi Shao,Changwen Qu,Jianwei Li,Shujuan Peng
Sensors.2018;18(9)3039
[DOI]
174A Deep Learning Approach for Breast Invasive Ductal Carcinoma Detection and Lymphoma Multi-Classification in Histological Images
Nadia Brancati,Giuseppe De Pietro,Maria Frucci,Daniel Riccio
IEEE Access.2019;7(9)44709
[DOI]
175Inconsistent Performance of Deep Learning Models on Mammogram Classification
Xiaoqin Wang,Gongbo Liang,Yu Zhang,Hunter Blanton,Zachary Bessinger,Nathan Jacobs
Journal of the American College of Radiology.2020;17(6)796
[DOI]
176Inconsistent Performance of Deep Learning Models on Mammogram Classification
Matko Saric,Mladen Russo,Maja Stella,Marjan Sikora
Journal of the American College of Radiology.2019;17(6)1
[DOI]
177Deep-Learning–Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data
Zixiao Lu,Siwen Xu,Wei Shao,Yi Wu,Jie Zhang,Zhi Han,Qianjin Feng,Kun Huang
JCO Clinical Cancer Informatics.2020;17(4)480
[DOI]
178Deep-Learning–Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data
Alexander Effland,Michael Hölzel,Teresa Klatzer,Erich Kobler,Jennifer Landsberg,Leonie Neuhäuser,Thomas Pock,Martin Rumpf
JCO Clinical Cancer Informatics.2018;17(4)334
[DOI]
179Deep-Learning–Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data
Kamal Jnawali,Bhargava Chinni,Vikram Dogra,Saugata Sinha,Navalgund Rao,Nicole V. Ruiter,Brett C. Byram
JCO Clinical Cancer Informatics.2019;17(4)51
[DOI]
180Culture codes of scientific concepts in global scientific online discourse
Dina I. Spicheva,Ekaterina V. Polyanskaya
AI & SOCIETY.2020;17(4)51
[DOI]
181Culture codes of scientific concepts in global scientific online discourse
Mahesh Gour,Sweta Jain,Raghav Agrawal
AI & SOCIETY.2020;1148(4)243
[DOI]
182A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda,Atsuto Maki,Maciej A. Mazurowski
Neural Networks.2018;106(4)249
[DOI]
183Mitochondrial Organelle Movement Classification (Fission and Fusion) via Convolutional Neural Network Approach
Muhammad Shahid Iqbal,Bin Luo,Rashid Mehmood,Mayda Abdullateef Alrige,Riad Alharbey
IEEE Access.2019;7(4)86570
[DOI]
184Mitochondrial Organelle Movement Classification (Fission and Fusion) via Convolutional Neural Network Approach
Nilanjana Dutta Roy,Arindam Biswas,Souvik Ghosh,Rajarshi Lahiri,Abhijit Mitra,Manabendra Dutta Choudhury
IEEE Access.2019;11942(4)32
[DOI]
185Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine
Kipp W. Johnson,Khader Shameer,Benjamin S. Glicksberg,Ben Readhead,Partho P. Sengupta,Johan L.M. Björkegren,Jason C. Kovacic,Joel T. Dudley
JACC: Basic to Translational Science.2017;2(3)311
[DOI]
186Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine
Xipeng Pan,Lingqiao Li,Huihua Yang,Zhenbing Liu,Yubei He,Zhongming Li,Yongxian Fan,Zhiwei Cao,Longhao Zhang
JACC: Basic to Translational Science.2020;810(3)85
[DOI]
187Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine
Pablo Guillén-Rondon,Melvin Robinson,Jerry Ebalunode
JACC: Basic to Translational Science.2019;979(3)33
[DOI]
188Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine
Chonho Lee,Seiya Murata,Kobo Ishigaki,Susumu Date
JACC: Basic to Translational Science.2017;979(3)181
[DOI]
189Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography
Yi Wang,Hao Zhang,Kum Ju Chae,Younhee Choi,Gong Yong Jin,Seok-Bum Ko
Multidimensional Systems and Signal Processing.2020;31(3)1163
[DOI]
190Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis
Yijiang Chen,Andrew Janowczyk,Anant Madabhushi
JCO Clinical Cancer Informatics.2020;31(4)221
[DOI]
191Pathology Image Analysis Using Segmentation Deep Learning Algorithms
Shidan Wang,Donghan M. Yang,Ruichen Rong,Xiaowei Zhan,Guanghua Xiao
The American Journal of Pathology.2019;189(9)1686
[DOI]
192Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images
Xiangxue Wang,Andrew Janowczyk,Yu Zhou,Rajat Thawani,Pingfu Fu,Kurt Schalper,Vamsidhar Velcheti,Anant Madabhushi
Scientific Reports.2017;7(1)1686
[DOI]
193Cardiac tissue engineering: state-of-the-art methods and outlook
Anh H. Nguyen,Paul Marsh,Lauren Schmiess-Heine,Peter J. Burke,Abraham Lee,Juhyun Lee,Hung Cao
Journal of Biological Engineering.2019;13(1)1686
[DOI]
194A computer-aided diagnosis system for differentiation and delineation of malignant regions on whole-slide prostate histopathology image using spatial statistics and multidimensional DenseNet
Chiao-Min Chen,Yao-Sian Huang,Pei-Wei Fang,Cher-Wei Liang,Ruey-Feng Chang
Medical Physics.2020;47(3)1021
[DOI]
195Artificial intelligence driven next-generation renal histomorphometry
Briana A. Santo,Avi Z. Rosenberg,Pinaki Sarder
Current Opinion in Nephrology and Hypertension.2020;29(3)265
[DOI]
196Digital Microscopy, Image Analysis, and Virtual Slide Repository
Famke Aeffner,Hibret A Adissu,Michael C Boyle,Robert D Cardiff,Erik Hagendorn,Mark J Hoenerhoff,Robert Klopfleisch,Susan Newbigging,Dirk Schaudien,Oliver Turner,Kristin Wilson
ILAR Journal.2018;59(1)66
[DOI]
197Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion
Jie Cai,Wen-guang He,Long Wang,Ke Zhou,Tian-xiu Wu
Scientific Reports.2019;9(1)66
[DOI]
198Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management
David T. Broome,C. Beau Hilton,Neil Mehta
Current Diabetes Reports.2020;20(2)66
[DOI]
199Epithelium segmentation and automated Gleason grading of prostate cancer via deep learning in label-free multiphoton microscopic images
Qinqin Yang,Zhexin Xu,Chenxi Liao,Jianyong Cai,Ying Huang,Hong Chen,Xuan Tao,Zheng Huang,Jianxin Chen,Jiyang Dong,Xiaoqin Zhu
Journal of Biophotonics.2020;13(2)66
[DOI]
200Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells
Ramraj Chandradevan,Ahmed A. Aljudi,Bradley R. Drumheller,Nilakshan Kunananthaseelan,Mohamed Amgad,David A. Gutman,Lee A. D. Cooper,David L. Jaye
Laboratory Investigation.2020;100(1)98
[DOI]
201Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells
Nitin N. Shirale
Laboratory Investigation.2018;100(1)1
[DOI]
202Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells
Adrien Foucart,Olivier Debeir,Christine Decaestecker
Laboratory Investigation.2018;100(1)1
[DOI]
203Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey?
Neil Mehta,Murthy V. Devarakonda
Journal of Allergy and Clinical Immunology.2018;141(6)2019
[DOI]
204Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks
Susan Sheehan,Seamus Mawe,Rachel E. Cianciolo,Ron Korstanje,J. Matthew Mahoney
The American Journal of Pathology.2019;189(9)1786
[DOI]
205Deep Learning for Whole Slide Image Analysis: An Overview
Neofytos Dimitriou,Ognjen Arandjelovic,Peter D. Caie
Frontiers in Medicine.2019;6(9)1786
[DOI]
206Deep Learning for Whole Slide Image Analysis: An Overview
Nelson Zange Tsaku,Sai Chandra Kosaraju,Tasmia Aqila,Mohammad Masum,Dae Hyun Song,Ananda M. Mondal,Hyun Min Koh,Mingon Kang
Frontiers in Medicine.2019;6(9)973
[DOI]
207FABnet: feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer
M. M. Fraz,S. A. Khurram,S. Graham,M. Shaban,M. Hassan,A. Loya,N. M. Rajpoot
Neural Computing and Applications.2019;6(9)973
[DOI]
208Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning
F. Klauschen,K.-R. Müller,A. Binder,M. Bockmayr,M. Hägele,P. Seegerer,S. Wienert,G. Pruneri,S. de Maria,S. Badve,S. Michiels,T.O. Nielsen,S. Adams,P. Savas,F. Symmans,S. Willis,T. Gruosso,M. Park,B. Haibe-Kains,B. Gallas,A.M. Thompson,I. Cree,C. Sotiriou,C. Solinas,M. Preusser,S.M. Hewitt,D. Rimm,G. Viale,S. Loi,S. Loibl,R. Salgado,C. Denkert
Seminars in Cancer Biology.2018;52(9)151
[DOI]
209Automatic cellularity assessment from post-treated breast surgical specimens
Mohammad Peikari,Sherine Salama,Sharon Nofech-Mozes,Anne L. Martel
Cytometry Part A.2017;91(11)1078
[DOI]
210Automatic cellularity assessment from post-treated breast surgical specimens
Oinam Vivek Singh,Prakash Choudhary,Khelchandra Thongam
Cytometry Part A.2020;1148(11)36
[DOI]
211Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks
Jia Qu,Nobuyuki Hiruta,Kensuke Terai,Hirokazu Nosato,Masahiro Murakawa,Hidenori Sakanashi
Journal of Healthcare Engineering.2018;2018(11)1
[DOI]
212Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks
Yusuf H. Roohani,Eric G. Kiss
Journal of Healthcare Engineering.2018;11039(11)3
[DOI]
213Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks
Zaneta Swiderska-Chadaj,Zhaoxuan Ma,Nathan Ing,Tomasz Markiewicz,Malgorzata Lorent,Szczepan Cierniak,Ann E. Walts,Beatrice S. Knudsen,Arkadiusz Gertych
Journal of Healthcare Engineering.2019;1011(11)13
[DOI]
214Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks
Rene Bidart,Alexander Wong
Journal of Healthcare Engineering.2019;11663(11)369
[DOI]
215Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks
Anibal Pedraza,Jaime Gallego,Samuel Lopez,Lucia Gonzalez,Arvydas Laurinavicius,Gloria Bueno
Journal of Healthcare Engineering.2017;723(11)839
[DOI]
216Advances in the computational and molecular understanding of the prostate cancer cell nucleus
Neil M. Carleton,George Lee,Anant Madabhushi,Robert W. Veltri
Journal of Cellular Biochemistry.2018;119(9)7127
[DOI]
217Proteomic investigations into resistance in colorectal cancer
David I. Cantor,Harish R. Cheruku,Jack Westacott,Joo-Shik Shin,Abidali Mohamedali,Seong Boem Ahn
Expert Review of Proteomics.2020;17(1)49
[DOI]
218Machine Learning and Veterinary Pathology: Be Not Afraid!
Krista M. D. La Perle
Veterinary Pathology.2019;56(4)506
[DOI]
219Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy
Gyuheon Choi,Young-Gon Kim,Haeyon Cho,Namkug Kim,Hyunna Lee,Kyung Chul Moon,Heounjeong Go
Modern Pathology.2020;56(4)506
[DOI]
220Guided Soft Attention Network for Classification of Breast Cancer Histopathology Images
Heechan Yang,Ji-Ye Kim,Hyongsuk Kim,Shyam P. Adhikari
IEEE Transactions on Medical Imaging.2020;39(5)1306
[DOI]
221Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma
Jennifer Hannig,Hendrik Schäfer,Jörg Ackermann,Marie Hebel,Tim Schäfer,Claudia Döring,Sylvia Hartmann,Martin-Leo Hansmann,Ina Koch,Jason A. Papin
PLOS Computational Biology.2020;16(1)e1007516
[DOI]
222A Nuclei Segmentation Research Based on Convolutional Neural Network
?? ?
Computer Science and Application.2018;08(11)1643
[DOI]
223A Multi-Organ Nucleus Segmentation Challenge
Neeraj Kumar,Ruchika Verma,Deepak Anand,Yanning Zhou,Omer Fahri Onder,Efstratios Tsougenis,Hao Chen,Pheng-Ann Heng,Jiahui Li,Zhiqiang Hu,Yunzhi Wang,Navid Alemi Koohbanani,Mostafa Jahanifar,Neda Zamani Tajeddin,Ali Gooya,Nasir Rajpoot,Xuhua Ren,Sihang Zhou,Qian Wang,Dinggang Shen,Cheng-Kun Yang,Chi-Hung Weng,Wei-Hsiang Yu,Chao-Yuan Yeh,Shuang Yang,Shuoyu Xu,Pak Hei Yeung,Peng Sun,Amirreza Mahbod,Gerald Schaefer,Isabella Ellinger,Rupert Ecker,Orjan Smedby,Chunliang Wang,Benjamin Chidester,That-Vinh Ton,Minh-Triet Tran,Jian Ma,Minh N. Do,Simon Graham,Quoc Dang Vu,Jin Tae Kwak,Akshaykumar Gunda,Raviteja Chunduri,Corey Hu,Xiaoyang Zhou,Dariush Lotfi,Reza Safdari,Antanas Kascenas,Alison O'Neil,Dennis Eschweiler,Johannes Stegmaier,Yanping Cui,Baocai Yin,Kailin Chen,Xinmei Tian,Philipp Gruening,Erhardt Barth,Elad Arbel,Itay Remer,Amir Ben-Dor,Ekaterina Sirazitdinova,Matthias Kohl,Stefan Braunewell,Yuexiang Li,Xinpeng Xie,Linlin Shen,Jun Ma,Krishanu Das Baksi,Mohammad Azam Khan,Jaegul Choo,Adrian Colomer,Valery Naranjo,Linmin Pei,Khan M. Iftekharuddin,Kaushiki Roy,Debotosh Bhattacharjee,Anibal Pedraza,Maria Gloria Bueno,Sabarinathan Devanathan,Saravanan Radhakrishnan,Praveen Koduganty,Zihan Wu,Guanyu Cai,Xiaojie Liu,Yuqin Wang,Amit Sethi
IEEE Transactions on Medical Imaging.2020;39(5)1380
[DOI]
224Same same but different: A Web-based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations
Joshua Kubach,Angelika Muhlebner-Fahrngruber,Figen Soylemezoglu,Hajime Miyata,Pitt Niehusmann,Mrinalini Honavar,Fabio Rogerio,Se-Hoon Kim,Eleonora Aronica,Rita Garbelli,Samuel Vilz,Alexander Popp,Stefan Walcher,Christoph Neuner,Michael Scholz,Stefanie Kuerten,Verena Schropp,Sebastian Roeder,Philip Eichhorn,Markus Eckstein,Axel Brehmer,Katja Kobow,Roland Coras,Ingmar Blumcke,Samir Jabari
Epilepsia.2020;61(3)421
[DOI]
225Cellular community detection for tissue phenotyping in colorectal cancer histology images
Sajid Javed,Arif Mahmood,Muhammad Moazam Fraz,Navid Alemi Koohbanani,Ksenija Benes,Yee-Wah Tsang,Katherine Hewitt,David Epstein,David Snead,Nasir Rajpoot
Medical Image Analysis.2020;63(3)101696
[DOI]
226Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning
Saima Rathore,Tamim Niazi,Muhammad Aksam Iftikhar,Ahmad Chaddad
Cancers.2020;12(3)578
[DOI]
227Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images
Wenyuan Li,Jiayun Li,Karthik V. Sarma,King Chung Ho,Shiwen Shen,Beatrice S. Knudsen,Arkadiusz Gertych,Corey W. Arnold
IEEE Transactions on Medical Imaging.2019;38(4)945
[DOI]
228Detecting breast cancer using artificial intelligence: Convolutional neural network
Avishek Choudhury,Sunanda Perumalla
Technology and Health Care.2020;38(4)1
[DOI]
229Detecting breast cancer using artificial intelligence: Convolutional neural network
Jun Xu,Chao Zhou,Bing Lang,Qingshan Liu
Technology and Health Care.2017;38(4)73
[DOI]
230Artificial Intelligence in Pathology
Hye Yoon Chang,Chan Kwon Jung,Junwoo Isaac Woo,Sanghun Lee,Joonyoung Cho,Sun Woo Kim,Tae-Yeong Kwak
Journal of Pathology and Translational Medicine.2019;53(1)1
[DOI]
231A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow
Zeynettin Akkus,Jason Cai,Arunnit Boonrod,Atefeh Zeinoddini,Alexander D. Weston,Kenneth A. Philbrick,Bradley J. Erickson
Journal of the American College of Radiology.2019;16(9)1318
[DOI]
232A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow
Seiya Murata,Chonho Lee,Chihiro Tanikawa,Susumu Date
Journal of the American College of Radiology.2017;16(9)1
[DOI]
233A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen A.W.M. van der Laak,Bram van Ginneken,Clara I. Sánchez
Medical Image Analysis.2017;42(9)60
[DOI]
234Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology
Sixian You,Yi Sun,Lin Yang,Jaena Park,Haohua Tu,Marina Marjanovic,Saurabh Sinha,Stephen A. Boppart
npj Precision Oncology.2019;3(1)60
[DOI]
235The Job Market Outlook for Residency Graduates: Clear Weather Ahead for the Butterflies?
Ralph Green,Michael A. Hogarth,Michael B. Prystowsky,Hooman H. Rashidi
Archives of Pathology & Laboratory Medicine.2018;142(4)435
[DOI]
236The Job Market Outlook for Residency Graduates: Clear Weather Ahead for the Butterflies?
Chunyang Niu,Xiang Li,Zhigang Zhao,Jidong Huo
Archives of Pathology & Laboratory Medicine.2019;142(4)1343
[DOI]
237The Job Market Outlook for Residency Graduates: Clear Weather Ahead for the Butterflies?
Manish Gupta,Chetna Das,Arnab Roy,Prashant Gupta,G. Radhakrishna Pillai,Kamlakar Patole
Archives of Pathology & Laboratory Medicine.2020;142(4)1293
[DOI]
238The Job Market Outlook for Residency Graduates: Clear Weather Ahead for the Butterflies?
Jiayu Wang,Ruoxiu Xiao,Lijing Jia,Xianmei Wang
Archives of Pathology & Laboratory Medicine.2019;11903(4)395
[DOI]
239The Job Market Outlook for Residency Graduates: Clear Weather Ahead for the Butterflies?
M. Sadeghi,I. Maldonado,N. Abele,J. Haybaeck,A. Boese,P. Poudel,M. Friebe
Archives of Pathology & Laboratory Medicine.2019;11903(4)7212
[DOI]
240Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors
Chang Gong,Robert A. Anders,Qingfeng Zhu,Janis M. Taube,Benjamin Green,Wenting Cheng,Imke H. Bartelink,Paolo Vicini,Bing Wang,Aleksander S. Popel
Frontiers in Oncology.2019;8(4)7212
[DOI]
241Whole-slide image focus quality: Automatic assessment and impact on ai cancer detection
Timo Kohlberger,Yun Liu,Melissa Moran,Po-HsuanCameron Chen,Trissia Brown,JasonD Hipp,CraigH Mermel,MartinC Stumpe
Journal of Pathology Informatics.2019;10(1)39
[DOI]
242Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides
Arkadiusz Gertych,Zaneta Swiderska-Chadaj,Zhaoxuan Ma,Nathan Ing,Tomasz Markiewicz,Szczepan Cierniak,Hootan Salemi,Samuel Guzman,Ann E. Walts,Beatrice S. Knudsen
Scientific Reports.2019;9(1)39
[DOI]
243Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides
Alison K. Cheeseman,Hamid Tizhoosh,Edward R. Vrscay
Scientific Reports.2019;11663(1)147
[DOI]
244Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides
Vladimir Khryashchev,Anton Lebedev,Olga Stepanova,Anastasiya Srednyakova
Scientific Reports.2020;175(1)295
[DOI]
245Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts
Guy Nir,Soheil Hor,Davood Karimi,Ladan Fazli,Brian F. Skinnider,Peyman Tavassoli,Dmitry Turbin,Carlos F. Villamil,Gang Wang,R. Storey Wilson,Kenneth A. Iczkowski,M. Scott Lucia,Peter C. Black,Purang Abolmaesumi,S. Larry Goldenberg,Septimiu E. Salcudean
Medical Image Analysis.2018;50(1)167
[DOI]
246Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks
Christian Matek,Simone Schwarz,Karsten Spiekermann,Carsten Marr
Nature Machine Intelligence.2019;1(11)538
[DOI]
247Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks
Sebastian Otalora,Oscar Perdomo,Manfredo Atzori,Mats Andersson,Ludwig Jacobsson,Martin Hedlund,Henning Muller
Nature Machine Intelligence.2018;1(11)843
[DOI]
248Survey of deep learning in breast cancer image analysis
Taye Girma Debelee,Friedhelm Schwenker,Achim Ibenthal,Dereje Yohannes
Evolving Systems.2020;11(1)143
[DOI]
249A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers
David Romo-Bucheli,Andrew Janowczyk,Hannah Gilmore,Eduardo Romero,Anant Madabhushi
Cytometry Part A.2017;91(6)566
[DOI]
250Artificial intelligence and its potential in oncology
Vaishali Y. Londhe,Bhavya Bhasin
Drug Discovery Today.2019;24(1)228
[DOI]
251Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape
Michael S. Landau,Liron Pantanowitz
Journal of the American Society of Cytopathology.2019;8(4)230
[DOI]
252Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape
Hyun Jung,Christian Suloway,Tianyi Miao,Elijah F. Edmondson,David R. Morcock,Claire Deleage,Yanling Liu,Jack R. Collins,Curtis Lisle
Journal of the American Society of Cytopathology.2018;8(4)1
[DOI]
253Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape
Vladimir Khryashchev,Anton Lebedev,Olga Stepanova,Anastasiya Srednyakova
Journal of the American Society of Cytopathology.2019;199(4)149
[DOI]
254Stacked sparse autoencoder and case-based postprocessing method for nucleus detection
Siqi Li,Huiyan Jiang,Jie Bai,Ye Liu,Yu-dong Yao
Neurocomputing.2019;359(4)494
[DOI]
255Stacked sparse autoencoder and case-based postprocessing method for nucleus detection
Sajid Javed,Arif Mahmood,Naoufel Werghi,Nasir Rajpoot
Neurocomputing.2019;359(4)342
[DOI]
256Beyond the microscope: interpreting renal biopsy findings in the era of precision medicine
Serena M. Bagnasco
American Journal of Physiology-Renal Physiology.2018;315(6)F1652
[DOI]
257Machine learning and feature selection for drug response prediction in precision oncology applications
Mehreen Ali,Tero Aittokallio
Biophysical Reviews.2019;11(1)31
[DOI]
258Validation of machine learning models to detect amyloid pathologies across institutions
Juan C. Vizcarra,Marla Gearing,Michael J. Keiser,Jonathan D. Glass,Brittany N. Dugger,David A. Gutman
Acta Neuropathologica Communications.2020;8(1)31
[DOI]
259Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer
Jon Whitney,German Corredor,Andrew Janowczyk,Shridar Ganesan,Scott Doyle,John Tomaszewski,Michael Feldman,Hannah Gilmore,Anant Madabhushi
BMC Cancer.2018;18(1)31
[DOI]
260Predicting Infectious Disease Using Deep Learning and Big Data
Sangwon Chae,Sungjun Kwon,Donghyun Lee
International Journal of Environmental Research and Public Health.2018;15(8)1596
[DOI]
261Predicting Infectious Disease Using Deep Learning and Big Data
Matthias Kohl,Christoph Walz,Florian Ludwig,Stefan Braunewell,Maximilian Baust
International Journal of Environmental Research and Public Health.2018;10882(8)903
[DOI]
262Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics
Zhichao Liu,Erika P. Portero,Yiren Jian,Yunjie Zhao,Rosemary M. Onjiko,Chen Zeng,Peter Nemes
Analytical Chemistry.2019;91(9)5768
[DOI]
263Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics
Hesham Alghodhaifi,Abdulmajeed Alghodhaifi,Mohammed Alghodhaifi
Analytical Chemistry.2019;91(9)374
[DOI]
264Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics
Azam Hamidinekoo,Reyer Zwiggelaar
Analytical Chemistry.2017;10553(9)213
[DOI]
265Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders
Mesut Togaçar,Burhan Ergen,Zafer Cömert
Medical Hypotheses.2020;135(9)109503
[DOI]
266Deep learning a boon for biophotonics?
Pranita Pradhan,Shuxia Guo,Oleg Ryabchykov,Juergen Popp,Thomas W. Bocklitz
Journal of Biophotonics.2020;13(6)109503
[DOI]
267Artificial intelligence in radiology
Ahmed Hosny,Chintan Parmar,John Quackenbush,Lawrence H. Schwartz,Hugo J. W. L. Aerts
Nature Reviews Cancer.2018;18(8)500
[DOI]
268Medical Image Analysis using Convolutional Neural Networks: A Review
Syed Muhammad Anwar,Muhammad Majid,Adnan Qayyum,Muhammad Awais,Majdi Alnowami,Muhammad Khurram Khan
Journal of Medical Systems.2018;42(11)500
[DOI]
269Medical Image Analysis using Convolutional Neural Networks: A Review
Hui Qu,Gregory Riedlinger,Pengxiang Wu,Qiaoying Huang,Jingru Yi,Subhajyoti De,Dimitris Metaxas
Journal of Medical Systems.2019;42(11)900
[DOI]
270A deep learning method for classifying mammographic breast density categories
Aly A. Mohamed,Wendie A. Berg,Hong Peng,Yahong Luo,Rachel C. Jankowitz,Shandong Wu
Medical Physics.2018;45(1)314
[DOI]
271Predicting cancer outcomes from histology and genomics using convolutional networks
Pooya Mobadersany,Safoora Yousefi,Mohamed Amgad,David A. Gutman,Jill S. Barnholtz-Sloan,José E. Velázquez Vega,Daniel J. Brat,Lee A. D. Cooper
Proceedings of the National Academy of Sciences.2018;115(13)E2970
[DOI]
272Discovering anomalous patterns in large digital pathology images
Sriram Somanchi,Daniel B. Neill,Anil V. Parwani
Statistics in Medicine.2018;37(25)3599
[DOI]
273Bringing 3D tumor models to the clinic - predictive value for personalized medicine
Kathrin Halfter,Barbara Mayer
Biotechnology Journal.2017;12(2)1600295
[DOI]
274Bringing 3D tumor models to the clinic - predictive value for personalized medicine
Ziqian Luo,Xiangrui Zeng,Zhipeng Bao,Min Xu
Biotechnology Journal.2019;12(2)1
[DOI]
  Feedback 
  Subscribe 
  Advertise 
  Search 
  Advanced Search 

Submit articles
Most popular articles
Joiu us as a reviewer
Email alerts
Recommend this journal
JPI Blogs