Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: A systematic review

Rodriguez, João Pedro Mazuco; Rodriguez, Rubens; Silva, Vitor Werneck Krauss; Kitamura, Felipe; Corradi, Gustavo Cesar Antônio; Marchi, Ana Carolina Bertoletti De; Rieder, Rafael

doi:10.1016/j.jpi.2022.100138

Cited by 29 publications

(24 citation statements)

References 53 publications

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“…The results on transfer ability are encouraging for subjectively assessed data containing other stain-tissue types and acquired by different scanners, even for very shallow networks [157]. While shallow networks seem to provide a more appealing 33 https://github.com/icbcbicc/FocusLiteNN ratio between computation time and classification accuracy than handcrafted feature-based methods, their behavior for specific stain-tissue types (e.g., IHC) and cytology images has been rarely studied. The datasets used for learning or evaluation are generally free of artifacts owing to slide preparation or manipulation.…”

Section: ) Discussionmentioning

confidence: 95%

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Brixtel¹,

Bougleux

Lézoray

et al. 2022

IEEE Access

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With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital revolution. Simultaneously, with the development of image analysis algorithms based on artificial intelligence tools, the application of computerized WSI analysis can now be expected. However, transferring such tools into clinical practice is very challenging as they must deal with many artifacts that can occur during sample preparation and digitization. Therefore, the quality of WSIs is of prime importance, and we propose a review of the state-of-the-art of computational approaches for quality control. In particular, we focus on WSI quality issues related to the presence of sample preparation artifacts, compression artifacts, color variations, and out-of-focus areas. An analysis of the monthly WSI clinical routine in a cytological laboratory confirms the importance of implementing quality control measures. Given this observation, we draw perspectives on how a computational quality process can be included in a computational pathology diagnosis pipeline.

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Section: ) Discussionmentioning

confidence: 95%

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Brixtel¹,

Bougleux

Lézoray

et al. 2022

IEEE Access

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“…There is already a wide body of literature describing AI applications in medicine, particularly in pathology. [5][6][7] A key factor that has catalyzed the interest in AI applications in pathology is the transition to digital pathology, where whole-slide imaging is gradually replacing glass slides and microscopes. 8 The value propositions of digital pathology are welldescribed in literature, 9 however, a key value proposition relevant to F I G U R E 1 An overview of the diagnostic workflow in hematopathology.…”

Section: The Role Of the Pathologist In Making A Diagnosismentioning

confidence: 99%

“…Understanding this fundamental distinction in light of technological, regulatory, financial, and human‐factor arguments, elaborating on tangible use cases for AI in pathology is crucial to form the basis for a meaningful discussion of the current state of the field. There is already a wide body of literature describing AI applications in medicine, particularly in pathology 5–7 . A key factor that has catalyzed the interest in AI applications in pathology is the transition to digital pathology, where whole‐slide imaging is gradually replacing glass slides and microscopes 8 .…”

Section: Introductionmentioning

confidence: 99%

Applied machine learning in hematopathology

Dehkharghanian

Tizhoosh

et al. 2023

Int J Lab Hematology

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An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology.

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“…Currently, cancer diagnosis occurs through the careful analysis of pathologists, who manually examine tissue biopsy samples; it is a laborious and time-consuming procedure that is affected by inter- and intra-operator variability. The development of high-resolution whole slide image (WSI) scanners has enabled the realization of algorithms that automatically perform an accurate and efficient histopathological diagnosis, alleviating the global shortage of trained pathologists [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability

et al. 2023

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The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work, we developed an explainable computer-aided diagnosis (CAD) system that can be used to support pathologists in the evaluation of tumor cellularity in breast histopathological slides. In particular, we compared an end-to-end DL approach that exploits the Mask R-CNN instance segmentation architecture with a two steps pipeline, where the features are extracted while considering the morphological and textural characteristics of the cell nuclei. Classifiers that are based on support vector machines and artificial neural networks are trained on top of these features in order to discriminate between tumor and non-tumor nuclei. Afterwards, the SHAP (Shapley additive explanations) explainable artificial intelligence technique was employed to perform a feature importance analysis, which led to an understanding of the features processed by the machine learning models for making their decisions. An expert pathologist validated the employed feature set, corroborating the clinical usability of the model. Even though the models resulting from the two-stage pipeline are slightly less accurate than those of the end-to-end approach, the interpretability of their features is clearer and may help build trust for pathologists to adopt artificial intelligence-based CAD systems in their clinical workflow. To further show the validity of the proposed approach, it has been tested on an external validation dataset, which was collected from IRCCS Istituto Tumori “Giovanni Paolo II” and made publicly available to ease research concerning the quantification of tumor cellularity.

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Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: A systematic review

Cited by 29 publications

References 53 publications

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Applied machine learning in hematopathology

Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability

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