Interpretable breast cancer classification using CNNs on mammographic images

Ann-Kristin Balve, Peter Hendrix
Proceedings of the fifth Conference on Health, Inference, and Learning, PMLR 248:410-426, 2024.

Abstract

Deep learning models have achieved promising results in breast cancer classification, yet their ’black-box’ nature raises interpretability concerns. This research addresses the crucial need to gain insights into the decision-making process of convolutional neural networks (CNNs) for mammogram classification, specifically focusing on the underlying reasons for the CNN’s predictions of breast cancer. For CNNs trained on the Mammographic Image Analysis Society (MIAS) dataset, we compared the post-hoc interpretability techniques LIME, Grad-CAM, and Kernel SHAP in terms of explanatory depth and computational efficiency. The results of this analysis indicate that Grad-CAM, in particular, provides comprehensive insights into the behavior of the CNN, revealing distinctive patterns in normal, benign, and malignant breast tissue. We discuss the implications of the current findings for the use of machine learning models and interpretation techniques in clinical practice.

Cite this Paper

BibTeX
@InProceedings{pmlr-v248-balve24a, title = {Interpretable breast cancer classification using CNNs on mammographic images}, author = {Balve, Ann-Kristin and Hendrix, Peter}, booktitle = {Proceedings of the fifth Conference on Health, Inference, and Learning}, pages = {410--426}, year = {2024}, editor = {Pollard, Tom and Choi, Edward and Singhal, Pankhuri and Hughes, Michael and Sizikova, Elena and Mortazavi, Bobak and Chen, Irene and Wang, Fei and Sarker, Tasmie and McDermott, Matthew and Ghassemi, Marzyeh}, volume = {248}, series = {Proceedings of Machine Learning Research}, month = {27--28 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v248/main/assets/balve24a/balve24a.pdf}, url = {https://proceedings.mlr.press/v248/balve24a.html}, abstract = {Deep learning models have achieved promising results in breast cancer classification, yet their ’black-box’ nature raises interpretability concerns. This research addresses the crucial need to gain insights into the decision-making process of convolutional neural networks (CNNs) for mammogram classification, specifically focusing on the underlying reasons for the CNN’s predictions of breast cancer. For CNNs trained on the Mammographic Image Analysis Society (MIAS) dataset, we compared the post-hoc interpretability techniques LIME, Grad-CAM, and Kernel SHAP in terms of explanatory depth and computational efficiency. The results of this analysis indicate that Grad-CAM, in particular, provides comprehensive insights into the behavior of the CNN, revealing distinctive patterns in normal, benign, and malignant breast tissue. We discuss the implications of the current findings for the use of machine learning models and interpretation techniques in clinical practice.} }
Endnote
%0 Conference Paper %T Interpretable breast cancer classification using CNNs on mammographic images %A Ann-Kristin Balve %A Peter Hendrix %B Proceedings of the fifth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2024 %E Tom Pollard %E Edward Choi %E Pankhuri Singhal %E Michael Hughes %E Elena Sizikova %E Bobak Mortazavi %E Irene Chen %E Fei Wang %E Tasmie Sarker %E Matthew McDermott %E Marzyeh Ghassemi %F pmlr-v248-balve24a %I PMLR %P 410--426 %U https://proceedings.mlr.press/v248/balve24a.html %V 248 %X Deep learning models have achieved promising results in breast cancer classification, yet their ’black-box’ nature raises interpretability concerns. This research addresses the crucial need to gain insights into the decision-making process of convolutional neural networks (CNNs) for mammogram classification, specifically focusing on the underlying reasons for the CNN’s predictions of breast cancer. For CNNs trained on the Mammographic Image Analysis Society (MIAS) dataset, we compared the post-hoc interpretability techniques LIME, Grad-CAM, and Kernel SHAP in terms of explanatory depth and computational efficiency. The results of this analysis indicate that Grad-CAM, in particular, provides comprehensive insights into the behavior of the CNN, revealing distinctive patterns in normal, benign, and malignant breast tissue. We discuss the implications of the current findings for the use of machine learning models and interpretation techniques in clinical practice.
APA
Balve, A. & Hendrix, P.. (2024). Interpretable breast cancer classification using CNNs on mammographic images. Proceedings of the fifth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 248:410-426 Available from https://proceedings.mlr.press/v248/balve24a.html.

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