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Frontiers in Bioengineering and Biotechnology.2018;10882(2)727
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107The 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
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108Machine Learning Methods for Histopathological Image Analysis
Daisuke Komura,Shumpei Ishikawa
Computational and Structural Biotechnology Journal.2018;16(4)34
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109Quantitative 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)34
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110Convolutional 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)34
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111Fusing Learned Representations from Riesz Filters and Deep CNN for Lung Tissue Classification
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Medical Image Analysis.2019;9(1)34
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112Automating the Paris System for urine cytopathology—A hybrid deep-learning and morphometric approach
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Cancer Cytopathology.2019;127(2)98
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113Automating the Paris System for urine cytopathology—A hybrid deep-learning and morphometric approach
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114Automatic 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(2)167
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115Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts
Sebastian Otalora,Oscar Perdomo,Manfredo Atzori,Mats Andersson,Ludwig Jacobsson,Martin Hedlund,Henning Muller
Medical Image Analysis.2018;50(2)843
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116Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts
Ziba Gandomkar,Patrick C. Brennan,Claudia Mello-Thoms,Elizabeth A. Krupinski
Medical Image Analysis.2018;50(2)54
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117A 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
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118Artificial intelligence and its potential in oncology
Vaishali Y. Londhe,Bhavya Bhasin
Drug Discovery Today.2019;24(1)228
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119Artificial 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;24(1)228
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120Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape
Chen Wang,Ji Bao,Hong Bu
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121Development 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
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122Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software
Hyun Jung,Christian Suloway,Tianyi Miao,Elijah F. Edmondson,David R. Morcock,Claire Deleage,Yanling Liu,Jack R. Collins,Curtis Lisle
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123Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software
Vladimir Khryashchev,Anton Lebedev,Olga Stepanova,Anastasiya Srednyakova
Virchows Archiv.2019;199(2)149
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124Stacked sparse autoencoder and case-based postprocessing method for nucleus detection
Siqi Li,Huiyan Jiang,Jie Bai,Ye Liu,Yu-dong Yao
Neurocomputing.2019;199(2)149
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125Beyond the microscope: interpreting renal biopsy findings in the era of precision medicine
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American Journal of Physiology-Renal Physiology.2018;315(6)F1652
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126Machine learning and feature selection for drug response prediction in precision oncology applications
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127Impact 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;11(1)31
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128Direct 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(1)1
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129Quantitative 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)1
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130Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer
Oscar Jimenez-del-Toro,Sebastian Otálora,Mats Andersson,Kristian Eurén,Martin Hedlund,Mikael Rousson,Henning Müller,Manfredo Atzori
BMC Cancer.2017;18(1)281
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131Predicting 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
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132Predicting 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
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133Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics
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Analytical Chemistry.2019;91(9)5768
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134Artificial intelligence in radiology
Ahmed Hosny,Chintan Parmar,John Quackenbush,Lawrence H. Schwartz,Hugo J. W. L. Aerts
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135Artificial intelligence in radiology
Rucha Tambe,Sarang Mahajan,Urmil Shah,Mohit Agrawal,Bhushan Garware
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136A 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
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137Predicting 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
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138Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images
Massimo Salvi,Filippo Molinari
BioMedical Engineering OnLine.2018;17(1)E2970
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139Discovering anomalous patterns in large digital pathology images
Sriram Somanchi,Daniel B. Neill,Anil V. Parwani
Statistics in Medicine.2018;37(25)3599
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140Training 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
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141Fluorescence-based quantification of nucleocytoplasmic transport
Joshua B. Kelley,Bryce M. Paschal
Methods.2019;157(2)106
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142Bringing 3D tumor models to the clinic - predictive value for personalized medicine
Kathrin Halfter,Barbara Mayer
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