22 Feb 2021
In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification with the formalization of the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context. After training two WSI classification architectures on Camelyon-16 WSI dataset, highlighting discriminative features learned, and validating our approach with pathologists, we propose a novel manner of computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances.
We measure the improvement using the tile-level AUC that we called Localization AUC, and show an improvement of more than 0.2. We also validate our results with a RemOve And Retrain (ROAR) measure. Then, after studying the impact of the number of features used for heat-map computation, we propose a corrective approach, relying on activation colocalization of selected features, that improves the performances and the stability of our proposed method.
Antoine Pirovano 1,2,*, Hippolyte Heuberger 1, Sylvain Berlemont 1, SaÏd Ladjal 2 and Isabelle Bloch 2,3
1 Keen Eye, 75012 Paris, France; email@example.com (H.H.); firstname.lastname@example.org (S.B.)
2 LTCI, Télécom Paris, Institut Polytechnique de Paris, 91120 Palaiseau, France; email@example.com (S.L.); firstname.lastname@example.org (I.B.)
3 Centre National de la Recherche Scientifique, Laboratoire d’Informatique de Paris 6, Sorbonne Université, 75005 Paris, France
* Correspondence: email@example.com