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Sadafi, A. ; Navab, N.* ; Marr, C.

Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-Based Multiple Instance Learning.

In: (Proceedings - International Symposium on Biomedical Imaging, 18-21 April 2023, Cartagena, Colombia). 345 E 47th St, New York, Ny 10017 Usa: Ieee, 2023. 5 (Proceedings - International Symposium on Biomedical Imaging ; 2023-April)
DOI
In many histopathology tasks, sample classification depends on morphological details in tissue or single cells that are only visible at the highest magnification. For a pathologist, this implies tedious zooming in and out, while for a computational decision support algorithm, it leads to the analysis of a huge number of small image patches per whole slide image (WSI). Attention-based multiple instance learning (MIL), where attention estimation is learned in a weakly supervised manner, has been successfully applied in computational histopathology, but it is challenged by large numbers of irrelevant patches, reducing its accuracy. Here, we present an active learning approach to the problem. Querying the expert to annotate regions of interest in a WSI guides the formation of high-attention regions for MIL. We train an attention-based MIL and calculate a confidence metric for every image in the dataset to select the most uncertain WSIs for expert annotation. We test our approach on the CAMELYON17 dataset classifying metastatic lymph node sections in breast cancer. With a novel attention guiding loss, this leads to an accuracy boost of the trained models with few regions annotated for each class. Active learning thus improves WSIs classification accuracy, leads to faster and more robust convergence, and speeds up the annotation process. It may in the future serve as an important contribution to train MIL models in the clinically relevant context of cancer classification in histopathology.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Active Learning ; Multiple Instance Learning ; Pathology ; Uncertainty Estimation
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Konferenztitel Proceedings - International Symposium on Biomedical Imaging
Konferzenzdatum 18-21 April 2023
Konferenzort Cartagena, Colombia
Quellenangaben Band: 2023-April, Heft: , Seiten: 5 Artikelnummer: , Supplement: ,
Verlag Ieee
Verlagsort 345 E 47th St, New York, Ny 10017 Usa
Nichtpatentliteratur Publikationen
Institut(e) Institute of AI for Health (AIH)
Förderungen European Research Council (ERC) under the European Union