Artificial Intelligence and Digital Pathology: Challenges and Opportunities

Tizhoosh, Hamid R.; Pantanowitz, Liron

doi:10.4103/jpi.jpi_53_18

Cited by 363 publications

(329 citation statements)

References 53 publications

(46 reference statements)

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“…Reimbursement of the costs of AI‐based diagnostic and prognostic/predictive assays is one of the major issues that affect the application of these assays in clinic [91]. In the USA, insurance companies standardize expenses on the basis of the current procedural terminology codes maintained by the American Medical Association and reported by medical professionals [92‐95].…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Jiang

Yue

Wang

et al. 2020

Cancer Communications

210

147

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The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.

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Section: Challenges and Perspectivesmentioning

confidence: 99%

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Jiang

Yue

Wang

et al. 2020

Cancer Communications

210

147

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“…As a result, the pathology community has begun to scan many slides resulting in the creation of large databases of whole slide images (WSIs). The emergence of deep learning and other artificial-intelligence (AI) methods and their impressive pattern recognition capabilities when applied to these digital databases has immensely added to the value proposition of digital pathology [1][2][3] . Computerized operations, such as segmentation of tissue fragments and cell nuclei, and classification of diseases and their grades become possible after pathology slides are digitized.…”

Section: Introductionmentioning

confidence: 99%

Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence

et al. 2020

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The emergence of digital pathology has opened new horizons for histopathology and related fields such as cytology. Computer programs and, in particular, artificial-intelligence algorithms, are able to operate on digitized slides to assist pathologists with diagnostic and theranostic tasks. Whereas machine learning involving classification and segmentation methods have obvious benefits for performing image analysis in pathology, image search represents an alternate and fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologist a novel and real-time approach to improve diagnostic accuracy through visual inspection of similar cases and computational majority vote for consensus building. In this study, we report the results from searching the largest public repository (The Cancer Genome Atlas [TCGA] program by National Cancer Institute, USA) of whole slide images from almost 11,000 patients depicting different types of malignancies. For the first time, we successfully indexed and searched almost 30,000 high-resolution digitized slides constituting 16 terabytes of data comprised of 20 million 1000x*1000 pixels image patches. The TCGA image database covers 25 anatomic sites and contains 32 cancer subtypes. High-performance storage and GPU power were employed for experimentation. The results were assessed with conservative "**majority voting"** to build consensus for subtype diagnosis through vertical search and demonstrated high accuracy values for both frozen sections slides (e.g., bladder urothelial carcinoma 93%, kidney renal clear cell carcinoma 97%, and ovarian serous cystadenocarcinoma 99%) and permanent histopathology slides (e.g., prostate adenocarcinoma 98%, skin cutaneous melanoma 99%, and thymoma 100%).The key finding of this validation study was that computational consensus appears to be possible for rendering diagnoses if a sufficiently large number of searchable cases are available for each cancer subtype. ⇤ Corresponding author: tizhoosh@uwaterloo.ca⇥ pathology.Content-based image search [8][9][10][11] implies that the input for search software is not text (e.g., disease description in a pathology report) but rather the input is an image such that the search and retrieval can be performed based on image pixels (visual content). Content-based image search is inherently unsupervised, which means that its design and implementation may not need manual delineation of a region of interest in the images 12-14 . More importantly, image search does not make any direct diagnostic decision on behalf of the pathologist; instead, it searches for similar images and retrieves them along with corresponding metadata (i.e., pathology reports), and displays them to the pathologist as decision support.Variability in the visual inspection of medical images is a well-known problem [15][16][17] . Both inter-and intra-observer variability may affect image assessment and subsequently the ensuing diagnosis [18][19][20][21] . A large body of work...

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“…The recent success has also made it clear that the intrinsic nature of current ANNs is based on uni-task orientation [41]. Besides, multiple ANNs cannot be easily integrated into a larger network in order to perform multiple tasks as is probably the case in human brains (see Fig.…”

Section: Motivationmentioning

confidence: 99%

Subtractive Perceptrons for Learning Images: A Preliminary Report

Tizhoosh

Kalra

Lifshitz

et al. 2019

2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)

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In recent years, artificial neural networks have achieved tremendous success for many vision-based tasks. However, this success remains within the paradigm of weak AI where networks, among others, are specialized for just one given task. The path toward strong AI, or Artificial General Intelligence, remains rather obscure. One factor, however, is clear, namely that the feed-forward structure of current networks is not a realistic abstraction of the human brain. In this preliminary work, some ideas are proposed to define a subtractive Perceptron (s-Perceptron), a graph-based neural network that delivers a more compact topology to learn one specific task. In this preliminary study, we test the s-Perceptron with the MNIST dataset, a commonly used image archive for digit recognition. The proposed network achieves excellent results compared to the benchmark networks that rely on more complex topologies.

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Artificial Intelligence and Digital Pathology: Challenges and Opportunities

Cited by 363 publications

References 53 publications

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence

Subtractive Perceptrons for Learning Images: A Preliminary Report

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