TY - GEN
T1 - Improving Interpretability for Computer-Aided Diagnosis Tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-Based Explanations
AU - Pirovano, Antoine
AU - Heuberger, Hippolyte
AU - Berlemont, Sylvain
AU - Ladjal, Saïd
AU - Bloch, Isabelle
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. While performances reach expert’s level, interpretability (highlight how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification. We formalize 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. We aim at explaining how the decision is made based on tile level scoring, how these tile scores are decided and which features are used and relevant for the task. 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 by more than 29% for tile level AUC.
AB - Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. While performances reach expert’s level, interpretability (highlight how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification. We formalize 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. We aim at explaining how the decision is made based on tile level scoring, how these tile scores are decided and which features are used and relevant for the task. 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 by more than 29% for tile level AUC.
KW - Explainability
KW - Heat-maps
KW - Histopathology
KW - Interpretability
KW - WSI classification
U2 - 10.1007/978-3-030-61166-8_5
DO - 10.1007/978-3-030-61166-8_5
M3 - Conference contribution
AN - SCOPUS:85092913443
SN - 9783030611651
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 43
EP - 53
BT - Interpretable and Annotation-Efficient Learning for Medical Image Computing - 3rd International Workshop, iMIMIC 2020, 2nd International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Cardoso, Jaime
A2 - Silva, Wilson
A2 - Cruz, Ricardo
A2 - Van Nguyen, Hien
A2 - Roysam, Badri
A2 - Heller, Nicholas
A2 - Henriques Abreu, Pedro
A2 - Pereira Amorim, Jose
A2 - Isgum, Ivana
A2 - Patel, Vishal
A2 - Zhou, Kevin
A2 - Jiang, Steve
A2 - Le, Ngan
A2 - Luu, Khoa
A2 - Sznitman, Raphael
A2 - Cheplygina, Veronika
A2 - Abbasi, Samaneh
A2 - Mateus, Diana
A2 - Trucco, Emanuele
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the 2nd International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -