Journal of Pathology Informatics Journal of Pathology Informatics
Contact us | Home | Login   |  Users Online: 1538  Print this pageEmail this pageSmall font sizeDefault font sizeIncrease font size 




 
Table of Contents    
REVIEW ARTICLE
J Pathol Inform 2018,  9:38

Artificial intelligence and digital pathology: Challenges and opportunities


1 Kimia Lab, University of Waterloo; Huron Digital Pathology, Engineering Department, St. Jacobs, ON, Canada
2 Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA 15261, USA

Date of Submission30-Jul-2018
Date of Acceptance27-Aug-2018
Date of Web Publication14-Nov-2018

Correspondence Address:
Prof. Hamid Reza Tizhoosh
Kimia Lab, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1
Canada
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_53_18

Rights and Permissions
   Abstract 


In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in digital pathology. Although opportunities are both manifest and tangible, there are clearly many challenges that need to be overcome in order to exploit the AI potentials in computational pathology. In this paper, we strive to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.

Keywords: Artificial intelligence, deep learning, digital pathology


How to cite this article:
Tizhoosh HR, Pantanowitz L. Artificial intelligence and digital pathology: Challenges and opportunities. J Pathol Inform 2018;9:38

How to cite this URL:
Tizhoosh HR, Pantanowitz L. Artificial intelligence and digital pathology: Challenges and opportunities. J Pathol Inform [serial online] 2018 [cited 2021 Dec 5];9:38. Available from: https://www.jpathinformatics.org/text.asp?2018/9/1/38/245402




   Introduction Top


We are witnessing a transformation in pathology as a result of the widespread adoption of whole slide imaging (WSI) in lieu of traditional light microscopes.[1],[2] Depicting microscopic pathology characteristics digitally presents new horizons in pathology. Access to digital slides facilitates remote primary diagnostic work, teleconsultation, workload efficiency and balancing, collaborations, central clinical trial review, image analysis, virtual education, and innovative research. Leveraging WSI technology, the computer vision and artificial intelligence (AI) communities have offered additional computational pathology possibilities including deep learning algorithms and image recognition. Artificial neural networks (ANNs) have witnessed tremendous progress mainly as a result of deep learning. Diverse deep architectures have been trained with large image datasets (e.g., The Cancer Genome  Atlas More Details or TCGA and ImageNet) to yield novel biomedical informatics discoveries[3] and perform impressive object recognition tasks. Whether AI will eventually replace, or how it can best assist pathologists, has emerged as a provocative topic.[4],[5],[6],[7] In this article, we discuss key challenges and opportunities related to exploiting digital pathology in the up-and-coming AI era.


   Challenges Top


In spite of the enthusiasm and amassed impressive results shared to date,[8],[9],[10],[11] there are clear obstacles that limit easy employment of AI methods in digital pathology. We discuss several compelling challenges that need to be tackled.

Challenge #1: Lack of labeled data

Most AI algorithms require a large set of good quality training images. These training images must ideally be “labeled” (i.e., annotated). This generally means that a pathologist needs to manually delineate the region of interest (i.e., anomalies or malignancy) in all images. Annotation is ideally best performed by experts. Besides the time constraint involved, manual annotations often also pose a financial bottleneck to app development. Crowdsourcing may be cheaper and quicker but has the potential to introduce noise. For pathologists, detailed annotation of large numbers of images may not only be boring but also can be particularly challenging when working with low resolution or blurry images, slow networks, and ambiguity of features. Active learning applied to annotation may alleviate this taxing task. At present, there are a small number of publicly available datasets that contain labeled images that can be employed for this purpose. For instance, with the Medical Image Computing and Computer Assisted Intervention Society 2014 brain tumor digital pathology challenge, digital histopathology image data of brain tumors were provided. For this competition, when classifying brain tumors, the target was to distinguish images of glioblastoma multiforme (GBM) from low-grade glioma (LGG). The training set had 22 LGG images and 23 GBM images, and the testing (validation) set had 40 images.[12] Another example is the Camelyon dataset that contains many digital slides (i.e., slides with pixel-level annotations and unlabeled slides as a test set) for automated detection of breast carcinoma metastases in hematoxylin and eosin (H and E)-stained whole slide images of lymph node sections.[13] Fortunately, datasets that emphasize the multiclass nature of tissue recognition are slowly emerging.[14] In addition, there are several methods that computational scientists can leverage to maximize limited training data such as data augmentation (i.e., artificially transforming original training images[15]).

Challenge #2: Pervasive variability

There are several basic types of tissue (e.g., epithelium, connective tissue, nervous tissue, and muscle). However, the actual number of patterns derived from these tissues from a computational perspective is nearly infinite if the histopathology images are to be “understood” by computer algorithms. Several tissue types build an organ that is also reflected in new textural variation of the basic tissue types. This extreme polymorphism makes recognizing tissues by image algorithms exceptionally challenging.[16],[17] Thus, the inherent architecture of deep AI requires many training cases for each variation. This, however, may not be readily available, especially as labeled data.

Challenge #3: Non-boolean nature of diagnostic tasks

Many published research papers deal with classification problems in digital pathology that deal largely with binary variables, having just two possible values such as “yes” or “no” (e.g., benign or malignant). This is a drastic simplification of the complex nature of diagnosis in pathology.[18] A pathology diagnosis employs several processes including cognition, understanding clinical context, perception, and empirical experience. Sometimes, pathologists use cautious language or descriptive terminology for difficult and rare cases. Such language has ramifications for potential monitoring and treatment.[19] Hence, binary language may only be desirable in easy, obvious cases. This is rarely the case in clinical practice.

Challenge #4: Dimensionality obstacle

WSI deals with gigapixel digital images of extremely large dimensions. Image sizes larger than 50,000 by 50,000 pixels are quite common. Deep ANNs, however, operate on much smaller image dimensions (i.e., not larger than 350 by 350 pixels). “Patching” [i.e., dividing an image into many small tiles, [Figure 1] is a potential solution for not just AI algorithms but also for general computer vision methods. However, even for patches, one generally needs to downsample them in order to be able to feed them into a deep network. A region smaller than 1.5 μm2 may not be suitable for many diagnostic purposes and this is, most of the time, at least 1000 by 1000 pixels. Downsampling these patches may result in loss of crucial information. On the other hand, deep nets with larger input sizes would need much deeper topology and much larger number of neurons making them even more difficult and perhaps impossible to train. Of note, patch-based ANNs have been shown to outperform image-based ANNs.[20]
Figure 1: Patching is generally used to represent large scans. For instance, every patch could be a 1000 pixel ×1000 pixel image at ×20

Click here to view


Challenge #5: Turing test dilemma

Alan Turing, one of the most renowned pioneers of AI, suggested measuring the intelligence of machines using a human evaluator.[21] The level of machine intelligence, according to Turing, is inversely proportional to the time that human evaluators would need to figure out that the answers to one's questions are coming from a machine and not from a human operator if the source of the answers is concealed to the evaluator. The Turing test declares that human evolution is the ultimate validation of AI; a machine is as intelligent as a human only if it can successfully and infinitely impostor a human.[22] In digital pathology, we may not know the Turing test explicitly, but everybody adheres to its core statement, namely that the pathologist is the ultimate evaluator if AI solutions are deployed into clinical workflow. Thus, full automation is probably neither possible, it seems, nor wise as the Turing test postulates.

Challenge #6: Uni-task orientation of weak artificial intelligence

Weak AI is a form of AI focused on a specific task. What we speak of today is mostly “weak AI,"[23] representing a collection of specialized algorithms that can perform a given task with high accuracy, provided we can feed them with a large set of training data. In contrast, with strong AI, also called artificial general intelligence (AGI),[24],[25] we expect algorithms with human-level intelligence, multitasking, and even consciousness as well as ethical cognition. Of course, the latter is still within scope in the distant future. Deep ANNs belong to the class of weak AI algorithms, as they are designed to perform only one task. That means we would need to separately train multiple AI solutions for tasks such as segmentation, classification, and search. Even for a given task like classification (the major domain for AI algorithms), one would need to design, develop, and train solutions for many anatomical sites. Needless to say, this would require tremendous resources.

Challenge #7: Affordability of required computational expenses

Deep AI solutions are heavily dependent on using Graphical Processing Units (GPUs), highly specialized electronic circuits for fast processing of pixel-based data (i.e., digital images and graphics). Training and using deep solutions on ordinary computers with Central Processing Units is prohibitively sluggish and hence impractical.[26] It is obvious that having access to GPU clusters is a must to deploy deep networks in practice.[27],[28],[29] Pathology laboratories, however, are already under immense financial pressure to adopt WSI technology, and acquiring and storing gigapixel histopathological scans is a formidable challenge to the adoption of digital pathology. Asking for GPUs, as a prerequisite for training or using deep AI solutions, is consequently going to be financially limiting in the foreseeable future.

Challenge #8: Adversarial attacks – The shakiness of deep decisions

Several reports in the literature have shown that one can “fool” a deep ANN. Targeted manipulation of a very small number of pixels inside an image, which is called an adversarial attack, can mislead a heavy-duty deep network.[30] Apparently, some deep networks that act as a nonlinear and complex #8220;lookup table” are prone to slipping into an adjacent cell of that (invisible) decision table that they implicitly store in their millions of weights. Such behavior is, of course, worrisome in the medical field. Does this imply that the minimal presence of noise or artifacts (e.g., tissue tears/folds, crushed cells, debris, and contamination[31],[32]) in a deep ANN can mistakenly be diagnosed as cancer? The research community has still to find out how to create deep networks robust enough to avoid such mishaps. Perhaps, such uncertainty is a new manifestation of the old problem of #8220;overfitting#8221; in AI where a big solution swallows (i.e., memorizes) a small problem. Even so, verification of this new type of error is more difficult to deal with.

Challenge #9: Lack of transparency and interpretability

Deep ANNs have demonstrated several impressive success stories in object and scene recognition. However, they have not removed one of the major drawbacks of ANNs when used as classifiers, which is lack of interoperability. Some consider ANNs to embody a “black box” after they are trained.[33],[34] Although researchers have started to investigate creative ways to explain the results of AI,[35] there is at present no established way to easily explain why a specific decision was made by a network when dealing with histopathology scans. In other words, the millions of multiplications and additions performed inside a deep ANN in order to provide an output (i.e., a decision) do not provide a verifiable path to understanding the rationale behind its decisions. This is generally unacceptable in the medical community, as physicians and other experts involved in the diagnostic field typically need to justify the underlying reasons for a specific decision. The pathway to a reliable diagnosis must be transparent and fully comprehensible. This is also important if a deep learning algorithm needs to be fixed (locked down) as well as obtains regulatory approval for its use in clinical practice.

Challenge #10: Realism of artificial intelligence

While there is currently much optimism that AI applied to pathology is going to soon deliver far-reaching benefits (e.g., increased efficiency such as automation, error reduction and greater diagnostic accuracy, and better patient safety), implementing these tools so that they function well in daily practice is going to be difficult to accomplish. Reports of AI failures in health care are not necessarily related to failed technology but rather difficulties deploying AI tools in practice.[36] Pathologists' buy-in to employ these tools, irrespective of whether they intend to aid or replace them in practice, will depend on three key factors: (1) ease of use (e.g., uncomplicated preimaging demands, agnostic input, and generalizable, scalable, understandable output), (2) financial return on investment associated with using the app, and (3) trust (e.g., evidence of performance).


   Opportunities Top


Opportunity #1: Deep features – Pretraining is better

Transfer learning has gained much attention in recent years.[37] Customarily, one trains a deep network with a large set of images in a specific domain and uses the acquired knowledge in a different domain by either using the deep network as a feature extractor or by minimally re-training it with a (small) set of images in the new domain to fine-tune it for the new purpose.[38],[39] Pretrained networks, hence, have clear potential for many domains including the medical field.[40] For instance, sentiment analysis in text documents in different domains can benefit from transfer learning, and features learned from a million natural images (animals, buildings, vehicles, etc.) may provide features for medical images. Moreover, some of the aforementioned challenges can be overcome if transfer learning is used instead of attempting to train a new network from scratch.

Opportunity #2: Handcrafted features – Do not forget computer vision

The success of deep learning in developing some AI algorithms has pushed many computer vision schemes aside, among others the role of incorporating handcrafted features. Many well-established feature extraction methods such as local binary patterns,[41] and more recently encoded local projections[42] have demonstrated to be at least on par with deep features and even better in some cases. Their behavior can be fully understood, their results can be interpreted (at least by humans), and their extraction does not need excessive computational resources for learning. Whereas deep learning and other AI algorithms are quite exotic technologies for the pathology community, using “projections” and other conventional technologies may be more in alignment with the knowledge of many medical professionals. Many computer vision methods that utilize handcrafted features (e.g., nuclear size and gland shape) can be much more easily employed in digital pathology to deliver high identification accuracies.[43],[44],[45],[46]

Opportunity #3: Generative frameworks: Learning to see and not judge

Most successful AI techniques belong to the class of discriminative models, methods that can classify data into different groups, but most commonly into two groups (e.g., malignant versus benign findings). Discriminative models are subject to most of the challenges we have already listed, most notably that their development needs labeled data. Generative models, in contrast, focus on learning to (re) produce data without making any decisions.[47],[48] Naïve Bayes, restricted Boltzmann machines, and generative adversarial networks are examples of generative methods. If an algorithm can generate image data, it must have understood the image to be able to generate it. In general, generative algorithms learn joint probability (the statistics that characterize the image features) and guess the label (say what is in the image), whereas discriminative models directly estimate the class label. Deep generative models have been used, among others, for interstitial lung disease classification and for functional magnetic resonance imaging analysis.[48]

Opportunity #4: Unsupervised learning: When we do not need labels

Prior success stories for deep solutions have led to overuse of supervised algorithms. These are algorithms that need labeled data, images in which regions of interest are manually delineated by human experts. Unsupervised learning, however, has been a pillar of AI for decades that has been almost shoved into oblivion in recent years perhaps because of the impressive success of supervised AI methods.[49],[50] We need to rediscover the potential of unsupervised algorithms, such as self-organizing maps[51] and hierarchical clustering,[52] and adequately integrate them into the workflow of routine pathology practice. Since labeling images is not part of the daily routine of pathologists, extracting features without supervision (i.e., labeled data) may be very valuable.[53]

Opportunity #5: Virtual peer review – Placing the pathologist in the center

Putting all of the challenges and opportunities together, it is obvious that the pathologist should be central to both algorithm development and execution; we need pathologists for the former to validate algorithm performance, while the latter will serve the pathologist with some extracted knowledge. Instead of making decisions on behalf of pathologists, smart algorithms could rather provide reliable information extracted from proven diagnosed cases in an archive when they are (anatomically/pathologically) similar to the relevant characteristics of the patient being examined. The task of finding similar cases (already diagnosed and treated by other colleagues) can be performed using the laboratory's archive, a regional archive, or even a national or global repository of vetted diagnosed cases. For instance, if a patient has a biopsy, then the diagnosis can be compared to a prior specimen for quality assurance purposes (e.g., comparing a cervix biopsy histologic diagnosis to a recent Pap test interpretation for real-time cytologic-histopathologic correlation). With AI, it may be more palatable to let ultimate decision-making reside with pathologists and in so doing provide them with as much extrinsic meaningful knowledge as possible to assist in this process. With respect to the problem of interobserver variability,[54],[55] accessing image data to facilitate consensus would be beneficial.[56],[57]

Opportunity #6: Automation

AI software tools, if exploited and implemented well, have the possibility of handling laborious and mundane tasks (e.g., counting mitoses and screening for easily identifiable cancer types) and simplifying complex tasks (e.g., triaging biopsies that need urgent attention and ordering appropriate stains upfront when indicated). For instance, it has been recently demonstrated for breast cancer that image retrieval for “malignant regions” that “can be easily recognized by pathologists” can be performed by AI methods with a sensitivity above 92%.[58] This can certainly contribute to reducing the workload of pathologists and assist with case triage.

Opportunity #7: Re-birth of the hematoxylin and eosin image

In recent years, we have witnessed an increase in molecular testing, sometimes in lieu of tissue morphologic evaluation. However, by grinding up tissue for such analyses, we risk losing valuable insight into histopathology (e.g., host stromal reaction to cancer[59]) and spatial relations (e.g., tumor microenvironment, immune response to neoplasia, and rejection in transplantation). With the advent of computational pathology,[60],[61] especially when combined with emerging technologies (e.g., multiplexing and three-dimensional imaging), we have the ability to more deeply analyze individual pixels of pathology images to unlock diagnostic, theranostic, and potentially untapped prognostic information. Moreover, most AI approaches to mitosis detection, segmentation, nucleus classification, and predicting Gleason scores have been using H and E-stained images.[62]

Opportunity #8: Making data science accessible to pathologists

AI has the potential to favorably modify the pathologist's role in medicine. Despite the perceived threat of AI, it is plausible that AI tools that generate and/or analyze big image data will be a boon to pathologists by increasing their value, efficiency, accuracy, and personal satisfaction.[63]


   Summary Top


The accelerated adoption of digital pathology in clinical practice has ushered in new horizons for both computer vision and AI.[64],[65] Because of recent success stories in image recognition for nonmedical applications, many researchers and entrepreneurs are convinced that AI in general and deep learning in particular may be able to assist with many tasks in digital pathology. However, there are no commercial AI-driven software tools available just yet. Hence, pathologist buy-in from the outset (i.e., even when developing algorithms) is critical to make sure that these eagerly anticipated software packages fill germane gaps without disrupting clinical workflow. What parts of the clinical workflow and which human tasks can be improved or may be even replaced by AI algorithms remains to be seen. The adoption of AI in pathology is certainly not going to be as straightforward as the current enthusiasm appears to suggest. Regulatory approval of AI tools is likely to significantly promote their adoption in clinical practice. The US Food and Drug Administration (FDA) has already approved AI apps in other fields such as ophthalmology and radiology.[66],[67] Such FDA approval provides reassurance to both clinicians and patients that an AI app is trustworthy for clinical use. For clinicians, this implies less personal liability when using this tool, greater chance of receiving reimbursement, and in pathology less of a burden on the laboratory for self-validation. However, for developers of deep learning algorithms destined to be submitted for regulatory clearance, greater documentation of their model and technical decisions is required. Furthermore, commercialization implications such as scaling and deployment of their tool will need to be taken into consideration. These factors, in turn, can drive up the cost of algorithm development. Unfortunately, the AI community has experienced several major setbacks in the past when promised performances could not be delivered leading to very pessimistic views on AI.[68],[69] The danger of overselling AI is still omnipresent. Nonetheless, there is clear potential for breakthrough with AI in medical imaging, particularly in digital pathology, if we suitably manage the strengths and pitfalls.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Pantanowitz L, Valenstein PN, Evans AJ, Kaplan KJ, Pfeifer JD, Wilbur DC, et al. Review of the current state of whole slide imaging in pathology. J Pathol Inform 2011;2:36.  Back to cited text no. 1
    
2.
Pantanowitz L, Parwani AV. Digital pathology. ASCP Press; 2017. p. 304. ISBN: 978-08189-6104.  Back to cited text no. 2
    
3.
Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep 2018;23:181-93.e7.  Back to cited text no. 3
    
4.
Sharma G, Carter A. Artificial intelligence and the pathologist: Future frenemies? Arch Pathol Lab Med 2017;141:622-3.  Back to cited text no. 4
    
5.
Holzinger A, Malle B, Kieseberg P, Roth PM, Müller H, Reihs R, et al. Towards the augmented pathologist: Challenges of explainable-ai in digital pathology. arXiv Preprint arXiv: 1712.06657; 2017.  Back to cited text no. 5
    
6.
Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017;19:221-48.  Back to cited text no. 6
    
7.
Wong ST. Is pathology prepared for the adoption of artificial intelligence? Cancer Cytopathol 2018;126:373-5.  Back to cited text no. 7
    
8.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8.  Back to cited text no. 8
    
9.
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017;318:2199-210.  Back to cited text no. 9
    
10.
Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A 2018;115:E2970-E2979.  Back to cited text no. 10
    
11.
Robertson S, Azizpour H, Smith K, Hartman J. Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. Transl Res 2018;194:19-35.  Back to cited text no. 11
    
12.
Xu Y, Jia Z, Wang LB, Ai Y, Zhang F, Lai M, et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics 2017;18:281.  Back to cited text no. 12
    
13.
Camelyon; 2016. Available from: https://www.camelyon16.grand-challenge.org/. [Last accessed on 2018 Apr 17].  Back to cited text no. 13
    
14.
Babaie M, Kalra S, Sriram A, Mitcheltree C, Zhu S, Khatami A, Rahnamayan S, Tizhoosh HR. Classification and retrieval of digital pathology scans: A new dataset. CVMI Workshop@ CVPR; 2017.  Back to cited text no. 14
    
15.
Ahmad J, Muhammad K, Baik SW. Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search. PLoS One 2017;12:e0183838.  Back to cited text no. 15
    
16.
Gurcan MN, Boucheron L, Can A, Madabhushi A, Rajpoot N, Yener B. Histopathological image analysis: A review. IEEE Rev Biomed Eng 2009;2:147.  Back to cited text no. 16
    
17.
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: A summary. Acta Neuropathol 2016;131:803-20.  Back to cited text no. 17
    
18.
Pena GP, Andrade-Filho JS. How does a pathologist make a diagnosis? Arch Pathol Lab Med 2009;133:124-32.  Back to cited text no. 18
    
19.
Elmore JG, Longton GM, Carney PA, Geller BM, Onega T, Tosteson AN, et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 2015;313:1122-32.  Back to cited text no. 19
    
20.
Hou L, Samaras D, Kurc TM, Gao Y, Davis JE, Saltz JH, et al. Patch-based convolutional neural network for whole slide tissue image classification. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016;2016:2424-33.  Back to cited text no. 20
    
21.
Turing A. Computing Machinery and intelligence. Mind 1950;59:433-60.  Back to cited text no. 21
    
22.
Warwick K, Shah H. Passing the turing test does not mean the end of humanity. Cognit Comput 2016;8:409-19.  Back to cited text no. 22
    
23.
Russell SJ, Norvig P. Artificial Intelligence: A Modern Approach. Malaysia: Pearson Education Limited; 2016.  Back to cited text no. 23
    
24.
Goertzel B. Artificial general intelligence. In: Pennachin C, editor. Artificial General Intelligence. Vol. 2. New York: Springer; 2007.  Back to cited text no. 24
    
25.
Everitt T, Goertzel B, Potapov A. Artificial general intelligence. Lecture Notes in Artificial Intelligence. Heidelberg: Springer; 2017.  Back to cited text no. 25
    
26.
Chen XW, Lin X. Big data deep learning: Challenges and perspectives. IEEE Access 2014;2:514-25.  Back to cited text no. 26
    
27.
Coates A, Huval B, Wang T, Wu D, Catanzaro B, Andrew N. Deep learning with COTS HPC systems. Int Conf Mach Learn 2013. p. 1337-45.  Back to cited text no. 27
    
28.
Andrade G, Ferreira R, Teodoro G, Rocha L, Saltz JH, Kurc T. Efficient execution of microscopy image analysis on CPU, GPU, and MIC equipped cluster systems. Computer Architecture and High Performance Computing (SBAC-PAD). 2014 IEEE 26th International Symposium on. IEEE. NIH Public Access; 2014. p. 89.  Back to cited text no. 28
    
29.
Campos V, Sastre F, Yagües M, Bellver M, Gir#243;-i-Nieto X, Torres J. Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster. Procedia Comput Sci 2017;108:315-24.  Back to cited text no. 29
    
30.
Athalye A, Sutskever I. Synthesizing robust adversarial examples. arXiv preprint arXiv: 1707.07397; 2017.  Back to cited text no. 30
    
31.
Rastogi V, Puri N, Arora S, Kaur G, Yadav L, Sharma R, et al. Artefacts: A diagnostic dilemma #8211; A review. J Clin Diagn Res 2013;7:2408-13.  Back to cited text no. 31
    
32.
Chatterjee S. Artefacts in histopathology. J Oral Maxillofac Pathol 2014;18:S111-6.  Back to cited text no. 32
[PUBMED]  [Full text]  
33.
Knight W. The dark secret at the heart of AI. Technol Rev 2017;120:54-63. Available from: http://www.technologyreview.com. Last accessed on 2018 Sep 22].  Back to cited text no. 33
    
34.
Pande V. Artificial intelligence's #8216;black box’ is nothing to fear. The New York Times; 2018.  Back to cited text no. 34
    
35.
Montavon G, Samek W, Müller KR. Methods for interpreting and understanding deep neural networks. Digit Signal Proc 2018;73:1-15.  Back to cited text no. 35
    
36.
Freedman DH. A reality check for IBM's AI ambitions. Technol Rev 2017. Available from: http://www.technologyreview.com. [Last accessed on 2018 Sep 22].  Back to cited text no. 36
    
37.
Bengio Y. Deep learning of representations for unsupervised and transfer learning. Proceedings of ICML Workshop on Unsupervised and Transfer Learning. Edinburgh, Scotlan 2012;27:17-36.  Back to cited text no. 37
    
38.
Bar Y, Diamant I, Wolf L, Greenspan H. Deep learning with non-medical training used for chest pathology identification. Medical Imaging 2015: Computer-Aided Diagnosis. Vol. 9414. International Society for Optics and Photonics; 2015. p. 94140V.  Back to cited text no. 38
    
39.
Kieffer B, Babaie M, Kalra S, Tizhoosh HR. Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks. arXiv preprint arXiv: 1710.05726; 2017.  Back to cited text no. 39
    
40.
Chen H, Cui S, Li S. Application of transfer learning approaches in multimodal wearable human activity recognition. arXiv Preprint arXiv: 1707.02412; 2017.  Back to cited text no. 40
    
41.
Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 2002;24:971-87.  Back to cited text no. 41
    
42.
Tizhoosh H, Babaie M. Representing medical images with encoded local projections. IEEE Trans Biomed Eng 2018;65:2267-77.  Back to cited text no. 42
    
43.
Alhindi TJ, Kalra S, Ng KH, Afrin A, Tizhoosh HR. Comparing LBP, HOG and deep features for classification of histopathology images. arXiv preprint arXiv: 1805.05837; 2018.  Back to cited text no. 43
    
44.
Madabhushi A, Lee G. Image analysis and machine learning in digital pathology: Challenges and opportunities. Med Image Anal 2016;33:170-5.  Back to cited text no. 44
    
45.
Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J Pathol Inform 2016;7:29.  Back to cited text no. 45
[PUBMED]  [Full text]  
46.
Wang H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M, et al. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imaging (Bellingham) 2014;1:034003.  Back to cited text no. 46
    
47.
Susskind J, Mnih V, Hinton G. On deep generative models with applications to recognition. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference. IEEE; 2011.  Back to cited text no. 47
    
48.
Salakhutdinov R. Learning deep generative models. Ann Rev Stat Appl 2015;2:361-85.  Back to cited text no. 48
    
49.
Fergus R, Perona P, Zisserman A. Object class recognition by unsupervised scale-invariant learning. Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference. Vol. 2. Madison WI, USA: IEEE; 2003.  Back to cited text no. 49
    
50.
Lee H, Grosse R, Ranganath R, Ng AY. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th Annual International Conference on Machine Learning. Montreal, Quebec, Canada: ACM; 2009. p. 609-16.  Back to cited text no. 50
    
51.
Kohonen T. The self-organizing map. Proc IEEE 1990;78:1464-80.  Back to cited text no. 51
    
52.
Murtagh F, Contreras P. Algorithms for hierarchical clustering: An overview. Wiley Interdiscip Rev 2012;2:86-97.  Back to cited text no. 52
    
53.
Arevalo J, Cruz-Roa A, Arias V, Romero E, González FA. An unsupervised feature learning framework for basal cell carcinoma image analysis. Artif Intell Med 2015;64:131-45.  Back to cited text no. 53
    
54.
Lawton TJ, Acs G, Argani P, Farshid G, Gilcrease M, Goldstein N, et al. Interobserver variability by pathologists in the distinction between cellular fibroadenomas and phyllodes tumors. Int J Surg Pathol 2014;22:695-8.  Back to cited text no. 54
    
55.
Williamson SR, Rao P, Hes O, Epstein JI, Smith SC, Picken MM, et al. Challenges in pathologic staging of renal cell carcinoma: A study of interobserver variability among urologic pathologists. Am J Surg Pathol 2018;42:1253-61.  Back to cited text no. 55
    
56.
Mazzanti M, Shirka E, Gjergo H, Hasimi E. Imaging, health record, and artificial intelligence: Hype or hope? Curr Cardiol Rep 2018;20:48.  Back to cited text no. 56
    
57.
Tizhoosh HR, Czarnota GJ. Fast barcode retrieval for consensus contouring. arXiv preprint arXiv: 1709.10197; 2017.  Back to cited text no. 57
    
58.
Zheng Y, Jiang Z, Zhang H, Xie F, Ma Y, Shi H, et al. Histopathological whole slide image analysis using context-based CBIR. IEEE Trans Med Imaging 2018;37:1641-52.  Back to cited text no. 58
    
59.
Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med 2011;3:108-13.  Back to cited text no. 59
    
60.
van Laak J, Rajpoot N, Vossen D. The Promise of Computational pathology: Part 1. The pathologist. 2018a. Available from: https://thepathologist.com. [Last accessed on 2018 Sep 22].  Back to cited text no. 60
    
61.
van Laak J, Rajpoot N, Vossen D. The promise of computational pathology: Part 2. The pathologist. 2018b. Available from: https://thepathologist.com. [Last accessed on 2018 Sep 22].  Back to cited text no. 61
    
62.
Litjens G, Kooi T, Bejnordi BE, Setio AA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60-88.  Back to cited text no. 62
    
63.
Jha S, Topol EJ. Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA 2016;316:2353-4.  Back to cited text no. 63
    
64.
Fornell D. How Artificial Intelligence Will Change Medical Imaging. Imaging Technology News; 2017. Available from: https://www.itnonline.com. [Last accessed on 2018 Sep 22].  Back to cited text no. 64
    
65.
Granter SR, Beck AH, Papke DJ Jr. AlphaGo, deep learning, and the future of the human microscopist. Arch Pathol Lab Med 2017;141:619-21.  Back to cited text no. 65
    
66.
FDA News Release. FDA Permits Marketing of Artificial Intelligence-Based Device to detect Certain Diabetes-Related Eye Problems. FDA; 2018a. Available from: http://www.fda.gov. [Last accessed on 2018 Apr 11].  Back to cited text no. 66
    
67.
FDA News Release. FDA Permits Marketing of Artificial Intelligence Algorithm for Aiding Providers in Detecting Wrist Fractures. FDFA; 2018b. Available from: http://www.fda.gov. [Last accessed on 2018 May 24].  Back to cited text no. 67
    
68.
Dreyfus H. What Computer Still Can#39;t Do: A Critique of Artificial Reason. Cambridge, MA, USA: MIT Press; 1972.  Back to cited text no. 68
    
69.
Dreyfus H, Dreyfus SE, Athanasiou T. Mind over machine. Simon and Schuster. The Free Press, New York; 2000.  Back to cited text no. 69
    


    Figures

  [Figure 1]


This article has been cited by
1 A systematic review on application of deep learning in digestive system image processing
Huangming Zhuang, Jixiang Zhang, Fei Liao
The Visual Computer. 2021;
[Pubmed] | [DOI]
2 Cancer diagnosis using artificial intelligence: a review
K Aditya Shastry, H A Sanjay
Artificial Intelligence Review. 2021;
[Pubmed] | [DOI]
3 Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices
Ling Tan, Ji Guo, Selvarajah Mohanarajah, Kun Zhou
Natural Hazards. 2021; 107(3): 2389
[Pubmed] | [DOI]
4 Searching Images for Consensus
Hamid R. Tizhoosh, Phedias Diamandis, Clinton J.V. Campbell, Amir Safarpoor, Shivam Kalra, Danial Maleki, Abtin Riasatian, Morteza Babaie
The American Journal of Pathology. 2021; 191(10): 1702
[Pubmed] | [DOI]
5 Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images
Madeleine S. Durkee, Rebecca Abraham, Marcus R. Clark, Maryellen L. Giger
The American Journal of Pathology. 2021; 191(10): 1693
[Pubmed] | [DOI]
6 Applying artificial intelligence for cancer immunotherapy
Zhijie Xu, Xiang Wang, Shuangshuang Zeng, Xinxin Ren, Yuanliang Yan, Zhicheng Gong
Acta Pharmaceutica Sinica B. 2021;
[Pubmed] | [DOI]
7 Disentangling prevalence induced biases in medical image decision-making
Jennifer S. Trueblood, Quentin Eichbaum, Adam C. Seegmiller, Charles Stratton, Payton O'Daniels, William R. Holmes
Cognition. 2021; 212: 104713
[Pubmed] | [DOI]
8 Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer
Simon M. Thomas, James G. Lefevre, Glenn Baxter, Nicholas A. Hamilton
Medical Image Analysis. 2021; 68: 101915
[Pubmed] | [DOI]
9 Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides
Abtin Riasatian, Morteza Babaie, Danial Maleki, Shivam Kalra, Mojtaba Valipour, Sobhan Hemati, Manit Zaveri, Amir Safarpoor, Sobhan Shafiei, Mehdi Afshari, Maral Rasoolijaberi, Milad Sikaroudi, Mohd Adnan, Sultaan Shah, Charles Choi, Savvas Damaskinos, Clinton JV Campbell, Phedias Diamandis, Liron Pantanowitz, Hany Kashani, Ali Ghodsi, H.R. Tizhoosh
Medical Image Analysis. 2021; 70: 102032
[Pubmed] | [DOI]
10 A survey on active learning and human-in-the-loop deep learning for medical image analysis
Samuel Budd, Emma C. Robinson, Bernhard Kainz
Medical Image Analysis. 2021; 71: 102062
[Pubmed] | [DOI]
11 Whole-slide imaging in cytopathology: state of the art and future directions
Stefano Marletta, Darren Treanor, Albino Eccher, Liron Pantanowitz
Diagnostic Histopathology. 2021; 27(11): 425
[Pubmed] | [DOI]
12 LSE–Lancet Commission on the future of the NHS: re-laying the foundations for an equitable and efficient health and care service after COVID-19
Michael Anderson, Emma Pitchforth, Miqdad Asaria, Carol Brayne, Barbara Casadei, Anita Charlesworth, Angela Coulter, Bryony Dean Franklin, Cam Donaldson, Michael Drummond, Karen Dunnell, Margaret Foster, Ruth Hussey, Paul Johnson, Charlotte Johnston-Webber, Martin Knapp, Gavin Lavery, Marcus Longley, Jill Macleod Clark, Azeem Majeed, Martin McKee, John N Newton, Ciaran O'Neill, Rosalind Raine, Mike Richards, Aziz Sheikh, Peter Smith, Andrew Street, David Taylor, Richard G Watt, Moira Whyte, Michael Woods, Alistair McGuire, Elias Mossialos
The Lancet. 2021; 397(10288): 1915
[Pubmed] | [DOI]
13 Artificial intelligence and computational pathology
Miao Cui, David Y. Zhang
Laboratory Investigation. 2021; 101(4): 412
[Pubmed] | [DOI]
14 Deep convolutional neural network-based algorithm for muscle biopsy diagnosis
Yoshinori Kabeya, Mariko Okubo, Sho Yonezawa, Hiroki Nakano, Michio Inoue, Masashi Ogasawara, Yoshihiko Saito, Jantima Tanboon, Luh Ari Indrawati, Theerawat Kumutpongpanich, Yen-Lin Chen, Wakako Yoshioka, Shinichiro Hayashi, Toshiya Iwamori, Yusuke Takeuchi, Reitaro Tokumasu, Atsushi Takano, Fumihiko Matsuda, Ichizo Nishino
Laboratory Investigation. 2021;
[Pubmed] | [DOI]
15 Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer
Jianghua Wu, Changling Liu, Xiaoqing Liu, Wei Sun, Linfeng Li, Nannan Gao, Yajun Zhang, Xin Yang, Junjie Zhang, Haiyue Wang, Xinying Liu, Xiaozheng Huang, Yanhui Zhang, Runfen Cheng, Kaiwen Chi, Luning Mao, Lixin Zhou, Dongmei Lin, Shaoping Ling
Modern Pathology. 2021;
[Pubmed] | [DOI]
16 Digital pathology and artificial intelligence in translational medicine and clinical practice
Vipul Baxi, Robin Edwards, Michael Montalto, Saurabh Saha
Modern Pathology. 2021;
[Pubmed] | [DOI]
17 Integrating digital pathology into clinical practice
Matthew G. Hanna, Orly Ardon, Victor E. Reuter, Sahussapont Joseph Sirintrapun, Christine England, David S. Klimstra, Meera R. Hameed
Modern Pathology. 2021;
[Pubmed] | [DOI]
18 A generalized deep learning framework for whole-slide image segmentation and analysis
Mahendra Khened, Avinash Kori, Haran Rajkumar, Ganapathy Krishnamurthi, Balaji Srinivasan
Scientific Reports. 2021; 11(1)
[Pubmed] | [DOI]
19 CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance
Sara P. Oliveira, Pedro C. Neto, João Fraga, Diana Montezuma, Ana Monteiro, João Monteiro, Liliana Ribeiro, Sofia Gonçalves, Isabel M. Pinto, Jaime S. Cardoso
Scientific Reports. 2021; 11(1)
[Pubmed] | [DOI]
20 A pyramidal deep learning pipeline for kidney whole-slide histology images classification
Hisham Abdeltawab, Fahmi Khalifa, Mohammed Ghazal, Liang Cheng, Dibson Gondim, Ayman El-Baz
Scientific Reports. 2021; 11(1)
[Pubmed] | [DOI]
21 Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer
Sung Hak Lee, In Hye Song, Hyun-Jong Jang
International Journal of Cancer. 2021; 149(3): 728
[Pubmed] | [DOI]
22 Sliding window based deep ensemble system for breast cancer classification
Amin Alqudah, Ali Mohammad Alqudah
Journal of Medical Engineering & Technology. 2021; 45(4): 313
[Pubmed] | [DOI]
23 The Texas Society of Pathologists: molded by the legacy of pathology and focused on excellence in medicine for 100 years and beyond
L. Maximilian Buja
Baylor University Medical Center Proceedings. 2021; 34(1): 199
[Pubmed] | [DOI]
24 An empirical analysis of machine learning frameworks for digital pathology in medical science
S.K.B. Sangeetha, R Dhaya, Dhruv T Shah, R Dharanidharan, K. Praneeth Sai Reddy
Journal of Physics: Conference Series. 2021; 1767(1): 012031
[Pubmed] | [DOI]
25 The Value of Artificial Intelligence in Laboratory Medicine
Ketan Paranjape, Michiel Schinkel, Richard D Hammer, Bo Schouten, R S Nannan Panday, Paul W G Elbers, Mark H H Kramer, Prabath Nanayakkara
American Journal of Clinical Pathology. 2021; 155(6): 823
[Pubmed] | [DOI]
26 The Utility of Unsupervised Machine Learning in Anatomic Pathology
Ewen D McAlpine, Pamela Michelow, Turgay Celik
American Journal of Clinical Pathology. 2021;
[Pubmed] | [DOI]
27 Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology
Romain Mormont, Pierre Geurts, Raphael Maree
IEEE Journal of Biomedical and Health Informatics. 2021; 25(2): 412
[Pubmed] | [DOI]
28 Generalized Fixation Invariant Nuclei Detection Through Domain Adaptation Based Deep Learning
Mira Valkonen, Gunilla Hognas, G Steven Bova, Pekka Ruusuvuori
IEEE Journal of Biomedical and Health Informatics. 2021; 25(5): 1747
[Pubmed] | [DOI]
29 Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology
A. Corvo, H. S. Garcia Caballero, M. A. Westenberg, M. A. van Driel, J. J. van Wijk
IEEE Transactions on Visualization and Computer Graphics. 2021; 27(10): 3851
[Pubmed] | [DOI]
30 SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
Nicholas Petrick, Shazia Akbar, Kenny H. Cha, Sharon Nofech-Mozes, Berkman Sahiner, Marios A. Gavrielides, Jayashree Kalpathy-Cramer, Karen Drukker, Anne L. Martel, for the BreastPathQ Challenge Group
Journal of Medical Imaging. 2021; 8(03)
[Pubmed] | [DOI]
31 Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers
Julien Calderaro, Jakob Nikolas Kather
Gut. 2021; 70(6): 1183
[Pubmed] | [DOI]
32 Role of digital pathology in diagnostic histopathology in the response to COVID-19: results from a survey of experience in a UK tertiary referral hospital
Lisa Browning, Eve Fryer, Derek Roskell, Kieron White, Richard Colling, Jens Rittscher, Clare Verrill
Journal of Clinical Pathology. 2021; 74(2): 129
[Pubmed] | [DOI]
33 Current and future applications of artificial intelligence in pathology: a clinical perspective
Emad A Rakha, Michael Toss, Sho Shiino, Paul Gamble, Ronnachai Jaroensri, Craig H Mermel, Po-Hsuan Cameron Chen
Journal of Clinical Pathology. 2021; 74(7): 409
[Pubmed] | [DOI]
34 Delineating the breast cancer immune microenvironment in the era of multiplex immunohistochemistry/immunofluorescence
Tracy Z Tien, Justina N L W Lee, Jeffrey C T Lim, Xiao-Yang Chen, Aye Aye Thike, Puay Hoon Tan, Joe P S Yeong
Histopathology. 2021; 79(2): 139
[Pubmed] | [DOI]
35 Challenges Developing Deep Learning Algorithms in Cytology
Ewen David McAlpine, Liron Pantanowitz, Pamela M. Michelow
Acta Cytologica. 2021; 65(4): 301
[Pubmed] | [DOI]
36 Challenges and Opportunities for the Veterinary Pathologist in Biomedical Research
Mark James Hoenerhoff, David K. Meyerholz, Cory Brayton, Amanda P. Beck
Veterinary Pathology. 2021; 58(2): 258
[Pubmed] | [DOI]
37 Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review
Aleksandra Zuraw, Famke Aeffner
Veterinary Pathology. 2021; : 0300985821
[Pubmed] | [DOI]
38 Challenges of AI Adoption in the UAE Healthcare
Fatma Khamis Al Badi, Khawla Ali Alhosani, Fauzia Jabeen, Agata Stachowicz-Stanusch, Nazia Shehzad, Wolfgang AMANN
Vision: The Journal of Business Perspective. 2021; : 0972262920
[Pubmed] | [DOI]
39 Automated Quantification of Chronic Changes in the Kidney Biopsy: Another Step in the Right Direction
Jeffrey B. Hodgin, Laura H. Mariani
Journal of the American Society of Nephrology. 2021; 32(4): 767
[Pubmed] | [DOI]
40 Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study
Max Schmitt, Roman Christoph Maron, Achim Hekler, Albrecht Stenzinger, Axel Hauschild, Michael Weichenthal, Markus Tiemann, Dieter Krahl, Heinz Kutzner, Jochen Sven Utikal, Sebastian Haferkamp, Jakob Nikolas Kather, Frederick Klauschen, Eva Krieghoff-Henning, Stefan Fröhling, Christof von Kalle, Titus Josef Brinker
Journal of Medical Internet Research. 2021; 23(2): e23436
[Pubmed] | [DOI]
41 Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study
Pouyan Esmaeilzadeh, Tala Mirzaei, Spurthy Dharanikota
Journal of Medical Internet Research. 2021; 23(11): e25856
[Pubmed] | [DOI]
42 Multiplexed Immunohistochemistry and Digital Pathology as the Foundation for Next-Generation Pathology in Melanoma: Methodological Comparison and Future Clinical Applications
Yannick Van Herck, Asier Antoranz, Madhavi Dipak Andhari, Giorgia Milli, Oliver Bechter, Frederik De Smet, Francesca Maria Bosisio
Frontiers in Oncology. 2021; 11
[Pubmed] | [DOI]
43 Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review
Xiaoliang Xie, Xulin Wang, Yuebin Liang, Jingya Yang, Yan Wu, Li Li, Xin Sun, Pingping Bing, Binsheng He, Geng Tian, Xiaoli Shi
Frontiers in Oncology. 2021; 11
[Pubmed] | [DOI]
44 Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
Hyun-Jong Jang, In Hye Song, Sung Hak Lee
Applied Sciences. 2021; 11(2): 808
[Pubmed] | [DOI]
45 Artificial Intelligence in Dermatopathology: New Insights and Perspectives
Gerardo Cazzato, Anna Colagrande, Antonietta Cimmino, Francesca Arezzo, Vera Loizzi, Concetta Caporusso, Marco Marangio, Caterina Foti, Paolo Romita, Lucia Lospalluti, Francesco Mazzotta, Sebastiano Cicco, Gennaro Cormio, Teresa Lettini, Leonardo Resta, Angelo Vacca, Giuseppe Ingravallo
Dermatopathology. 2021; 8(3): 418
[Pubmed] | [DOI]
46 Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards
Maria Rosaria Giovagnoli, Daniele Giansanti
Healthcare. 2021; 9(7): 858
[Pubmed] | [DOI]
47 Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders
Maria Rosaria Giovagnoli, Sara Ciucciarelli, Livia Castrichella, Daniele Giansanti
Healthcare. 2021; 9(10): 1347
[Pubmed] | [DOI]
48 Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic
Ismini Papageorgiou, Daniel Bittner, Marios Nikos Psychogios, Stathis Hadjidemetriou
Symmetry. 2021; 13(11): 2168
[Pubmed] | [DOI]
49 Brightfield, fluorescence, and phase-contrast whole slide imaging via dual-LED autofocusing
Chengfei Guo, Zichao Bian, Soliman Alhudaithy, Shaowei Jiang, Yuji Tomizawa, Pengming Song, Tianbo Wang, Xiaopeng Shao
Biomedical Optics Express. 2021; 12(8): 4651
[Pubmed] | [DOI]
50 Deep learning approaches and applications in toxicologic histopathology: Current status and future perspectives
Shima Mehrvar, LaurenE Himmel, Pradeep Babburi, AndrewL Goldberg, Magali Guffroy, Kyathanahalli Janardhan, AmandaL Krempley, Bhupinder Bawa
Journal of Pathology Informatics. 2021; 12(1): 42
[Pubmed] | [DOI]
51 Dissecting the business case for adoption and implementation of digital pathology: A white paper from the digital pathology association
Giovanni Lujan, JenniferC Quigley, Douglas Hartman, Anil Parwani, Brian Roehmholdt, BryanVan Meter, Orly Ardon, MatthewG Hanna, Dan Kelly, Chelsea Sowards, Michael Montalto, Marilyn Bui, MarkD Zarella, Gerard Slootweg, JuanAntonio Retamero, MarkC Lloyd, James Madory, Doug Bowman
Journal of Pathology Informatics. 2021; 12(1): 17
[Pubmed] | [DOI]
52 Artificial intelligence in pathology: From prototype to product
André Homeyer, Johannes Lotz, LarsOle Schwen, Nick Weiss, Daniel Romberg, Henning Höfener, Norman Zerbe, Peter Hufnagl
Journal of Pathology Informatics. 2021; 12(1): 13
[Pubmed] | [DOI]
53 A Digital Pathology Solution to Resolve the Tissue Floater Conundrum
Liron Pantanowitz, Pamela Michelow, Scott Hazelhurst, Shivam Kalra, Charles Choi, Sultaan Shah, Morteza Babaie, Hamid R. Tizhoosh
Archives of Pathology & Laboratory Medicine. 2021; 145(3): 359
[Pubmed] | [DOI]
54 A review of microscopic analysis of blood cells for disease detection with AI perspective
Nilkanth Mukund Deshpande, Shilpa Gite, Rajanikanth Aluvalu
PeerJ Computer Science. 2021; 7: e460
[Pubmed] | [DOI]
55 Emerging role of deep learning-based artificial intelligence in tumor pathology
Yahui Jiang, Meng Yang, Shuhao Wang, Xiangchun Li, Yan Sun
Cancer Communications. 2020; 40(4): 154
[Pubmed] | [DOI]
56 Issues to Consider When Implementing Digital Pathology for Primary Diagnosis
Sylvia L. Asa, Andrew Evans
Archives of Pathology & Laboratory Medicine. 2020; 144(11): 1297
[Pubmed] | [DOI]
57 Introduction to digital pathology and computer-aided pathology
Soojeong Nam, Yosep Chong, Chan Kwon Jung, Tae-Yeong Kwak, Ji Youl Lee, Jihwan Park, Mi Jung Rho, Heounjeong Go
Journal of Pathology and Translational Medicine. 2020; 54(2): 125
[Pubmed] | [DOI]
58 Clinical Decision Support for Ovarian Carcinoma Subtype Classification: A Pilot Observer Study With Pathology Trainees
Marios A. Gavrielides, Meghan Miller, Ian S. Hagemann, Heba Abdelal, Zahra Alipour, Jie-Fu Chen, Behzad Salari, Lulu Sun, Huifang Zhou, Jeffrey D Seidman
Archives of Pathology & Laboratory Medicine. 2020; 144(7): 869
[Pubmed] | [DOI]
59 TissueWand, a rapid histopathology annotation tool
Martin Lindvall, Alexander Sanner, Fredrik Petré, Karin Lindman, Darren Treanor, Claes Lundström, Jonas Löwgren
Journal of Pathology Informatics. 2020; 11(1): 27
[Pubmed] | [DOI]
60 Value of public challenges for the development of pathology deep learning algorithms
DouglasJoseph Hartman, JeroenA. W. M. Van Der Laak, MetinN Gurcan, Liron Pantanowitz
Journal of Pathology Informatics. 2020; 11(1): 7
[Pubmed] | [DOI]
61 Hybrid autofluorescence and photoacoustic label-free microscopy for the investigation and identification of malignancies in ocular biopsies
George J. Tserevelakis, Kostas G. Mavrakis, Danai Pantazopoulou, Eleni Lagoudaki, Efstathios Detorakis, Giannis Zacharakis
Optics Letters. 2020; 45(20): 5748
[Pubmed] | [DOI]
62 Use of artificial intelligence in dermatology
Abhishek De, Aarti Sarda, Sachi Gupta, Sudip Das
Indian Journal of Dermatology. 2020; 65(5): 352
[Pubmed] | [DOI]
63 Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey
Sam Polesie, Phillip H. McKee, Jerad M. Gardner, Martin Gillstedt, Jan Siarov, Noora Neittaanmäki, John Paoli
Frontiers in Medicine. 2020; 7
[Pubmed] | [DOI]
64 Biobanking for glomerular diseases: a study design and protocol for KOrea Renal biobank NEtwoRk System TOward NExt-generation analysis (KORNERSTONE)
Eunjeong Kang, Yaerim Kim, Yong Chul Kim, Eunyoung Kim, Nankyoung Lee, Yeonghui Kim, Soojin Lee, Seungyeup Han, Misun Choe, Jin Ho Hwang, Sunhwa Lee, Ji In Park, Jung Tak Park, Beom Jin Lim, Jung Pyo Lee, Jung Nam An, Dong-Ryeol Ryu, Jung-Hyun Kim, Hee Gyung Kang, Hyun Soon Lee, Kyung Chul Moon, Kwon Wook Joo, Kook-Hwan Oh, Seung Seok Han, Hajeong Lee, Dong Ki Kim, Jung Pyo Lee, Jung Nam An, Jeonghwan Lee, Jeonghwan Park, Minjung Kim, Taekyoung Kim, Jinhyuk Kim, Jin Ho Hwang, Eun A. Park, Eunji Park, Ji In Park, Sun Hwa Lee, Soyeong Park, Nayoung Koh, Seungyeup Han, Yaerim Kim, Misun Choe, Yeonghui Kim, Dong Ki Kim, Kwon Wook Joo, Kook-Hwan Oh, Hajeong Lee, Seung Seok Han, Yong Chul Kim, Eunjeong Kang, Soojin Lee, Kyung Chul Moon, Hee Gyung Kang, Eunyoung Kim, Junghee Kim, Ji Hye Park, Ji Won Jeon, Jung Tak Park, Beom Jin Lim, Hyung Woo Kim, Young Su Joo, Kyungjoon Kim, Bo Young Nam, Eunyoung Kim, Nankyoung Lee
BMC Nephrology. 2020; 21(1)
[Pubmed] | [DOI]
65 Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives
Pouyan Esmaeilzadeh
BMC Medical Informatics and Decision Making. 2020; 20(1)
[Pubmed] | [DOI]
66 Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses
Liron Pantanowitz, Douglas Hartman, Yan Qi, Eun Yoon Cho, Beomseok Suh, Kyunghyun Paeng, Rajiv Dhir, Pamela Michelow, Scott Hazelhurst, Sang Yong Song, Soo Youn Cho
Diagnostic Pathology. 2020; 15(1)
[Pubmed] | [DOI]
67 Model Fooling Attacks Against Medical Imaging: A Short Survey
Tuomo Sipola, Samir Puuska, Tero Kokkonen
Information & Security: An International Journal. 2020; 46(2): 215
[Pubmed] | [DOI]
68 Addressing the challenges of artificial intelligence in medicine
Marcus Smith, Rachael C. Heath Jeffery
Internal Medicine Journal. 2020; 50(10): 1278
[Pubmed] | [DOI]
69 Review of the current state of digital image analysis in breast pathology
Martin C. Chang, Miralem Mrkonjic
The Breast Journal. 2020; 26(6): 1208
[Pubmed] | [DOI]
70 SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery
Siwen Hu-Lieskovan, Srabani Bhaumik, Kavita Dhodapkar, Jean-Charles J B Grivel, Sumati Gupta, Brent A Hanks, Sylvia Janetzki, Thomas O Kleen, Yoshinobu Koguchi, Amanda W Lund, Cristina Maccalli, Yolanda D Mahnke, Ruslan D Novosiadly, Senthamil R Selvan, Tasha Sims, Yingdong Zhao, Holden T Maecker
Journal for ImmunoTherapy of Cancer. 2020; 8(2): e000705
[Pubmed] | [DOI]
71 Impact 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; 31(5): 432
[Pubmed] | [DOI]
72 Feasibility of a deep learning algorithm to distinguish large cell neuroendocrine from small cell lung carcinoma in cytology specimens
Daniel Gonzalez, Robin L. Dietz, Liron Pantanowitz
Cytopathology. 2020; 31(5): 426
[Pubmed] | [DOI]
73 Artificial intelligence driven next-generation renal histomorphometry
Briana A. Santo, Avi Z. Rosenberg, Pinaki Sarder
Current Opinion in Nephrology and Hypertension. 2020; 29(3): 265
[Pubmed] | [DOI]
74 Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions
Anil V. Parwani, Mahul B. Amin
Advances in Anatomic Pathology. 2020; 27(4): 221
[Pubmed] | [DOI]
75 Advances in tissue-based imaging: impact on oncology research and clinical practice
Arman Rahman, Chowdhury Jahangir, Seodhna M. Lynch, Nebras Alattar, Claudia Aura, Niamh Russell, Fiona Lanigan, William M. Gallagher
Expert Review of Molecular Diagnostics. 2020; 20(10): 1027
[Pubmed] | [DOI]
76 Autofocusing technologies for whole slide imaging and automated microscopy
Zichao Bian, Chengfei Guo, Shaowei Jiang, Jiakai Zhu, Ruihai Wang, Pengming Song, Zibang Zhang, Kazunori Hoshino, Guoan Zheng
Journal of Biophotonics. 2020; 13(12)
[Pubmed] | [DOI]
77 Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning
Mingyu Chen, Bin Zhang, Win Topatana, Jiasheng Cao, Hepan Zhu, Sarun Juengpanich, Qijiang Mao, Hong Yu, Xiujun Cai
npj Precision Oncology. 2020; 4(1)
[Pubmed] | [DOI]
78 Pan-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)
[Pubmed] | [DOI]
79 Deep learning links histology, molecular signatures and prognosis in cancer
Nicolas Coudray, Aristotelis Tsirigos
Nature Cancer. 2020; 1(8): 755
[Pubmed] | [DOI]
80 Histo-ELISA technique for quantification and localization of tissue components
Zhongmin Li, Silvia Goebel, Andreas Reimann, Martin Ungerer
Scientific Reports. 2020; 10(1)
[Pubmed] | [DOI]
81 The future of pathology is digital
J.D. Pallua, A. Brunner, B. Zelger, M. Schirmer, J. Haybaeck
Pathology - Research and Practice. 2020; 216(9): 153040
[Pubmed] | [DOI]
82 Microscopic imaging of Inflammatory Bowel Disease (IBD) and Non-IBD Colitis on digital slides: The Italian Group-IBD Pathologists experience
Tiziana Salviato, Luca Reggiani Bonetti, Alessandro Mangogna, Giuseppe Leoncini, Moris Cadei, Flavio Caprioli, Alessandro Armuzzi, Marco Daperno, Vincenzo Villanacci
Pathology - Research and Practice. 2020; 216(11): 153189
[Pubmed] | [DOI]
83 A bird’s-eye view of deep learning in bioimage analysis
Erik Meijering
Computational and Structural Biotechnology Journal. 2020; 18: 2312
[Pubmed] | [DOI]
84 Artificial intelligence (AI) and big data in cancer and precision oncology
Zodwa Dlamini, Flavia Zita Francies, Rodney Hull, Rahaba Marima
Computational and Structural Biotechnology Journal. 2020; 18: 2300
[Pubmed] | [DOI]
85 Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images
Shivam Kalra, H.R. Tizhoosh, Charles Choi, Sultaan Shah, Phedias Diamandis, Clinton J.V. Campbell, Liron Pantanowitz
Medical Image Analysis. 2020; 65: 101757
[Pubmed] | [DOI]
86 Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning
Songhui Diao, Jiaxin Hou, Hong Yu, Xia Zhao, Yikang Sun, Ricardo Lewis Lambo, Yaoqin Xie, Lei Liu, Wenjian Qin, Weiren Luo
The American Journal of Pathology. 2020; 190(8): 1691
[Pubmed] | [DOI]



 

 
Top
  

    

 
  Search
 
   Browse articles
  
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
    Abstract
   Introduction
   Challenges
   Opportunities
   Summary
    References
    Article Figures

 Article Access Statistics
    Viewed12462    
    Printed136    
    Emailed0    
    PDF Downloaded2953    
    Comments [Add]    
    Cited by others 86    

Recommend this journal