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Article Dans Une Revue Medical Image Analysis Année : 2023

MS-CLAM: Mixed Supervision for the classification and localization of tumors in Whole Slide Images

Résumé

Given the size of digitized Whole Slide Images (WSIs), it is generally laborious and time-consuming for pathologists to exhaustively delineate objects within them, especially with datasets containing hundreds of slides to annotate. Most of the time, only slide-level labels are available, giving rise to the development of weakly-supervised models. However, it is often difficult to obtain from such models accurate object localization, e.g., patches with tumor cells in a tumor detection task, as they are mainly designed for slide-level classification. Using the attention-based deep Multiple Instance Learning (MIL) model as our base weakly-supervised model, we propose to use mixed supervision-i.e., the use of both slide-level and patch-level labels-to improve both the classification and the localization performances of the original model, using only a limited amount of patch-level labeled slides. In addition, we propose an attention loss term to regularize the attention between key instances, and a paired batch method to create balanced batches for the model. First, we show that the changes made to the model already improve its performance and interpretability in the weakly-supervised setting. Furthermore, when using only between 12 and 62% of the total available patch-level annotations, we can reach performance close to fully-supervised models on the tumor classification datasets DigestPath2019 and Camelyon16.
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Dates et versions

hal-03972289 , version 1 (03-02-2023)

Identifiants

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Paul Tourniaire, Marius Ilie, Paul Hofman, Nicholas Ayache, Hervé Delingette. MS-CLAM: Mixed Supervision for the classification and localization of tumors in Whole Slide Images. Medical Image Analysis, inPress, 85, pp.102763. ⟨10.1016/j.media.2023.102763⟩. ⟨hal-03972289⟩
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