Causal ATTention Multiple Instance Learning for Whole Slide Image Classification

Xiaochun Wu, Haitao Wang, Hejun Wu
Proceedings of the 16th Asian Conference on Machine Learning, PMLR 260:984-999, 2025.

Abstract

We propose a new multiple instance learning (MIL) method called Causal ATTention Multiple Instance Learning (CATTMIL) to alleviate the dataset bias for more accurate classification of whole slide images (WSIs). There are different kinds of dataset bias due to confounders that are rooted in data generation and/or pre-training dataset of MIL. Confounders might mislead MIL models to learn spurious correlations between instances and bag label. Such spurious correlations, in turn, impede the generalization ability of models and hurt the final performance. To fight against the negative impacts of confounders, CATTMIL exploits the causal intervention using the front-door adjustment with a Causal ATTention (CATT) mechanism. This enables CATTMIL to remove the spurious correlations so as to estimate the causal effect of instances on the bag label. Unlike previous deconfounded MIL methods, our CATTMIL does not need to approximate confounder values. Therefore, CATTMIL is able to bring further performance boosting to existing schemes and achieve the state-of-the-art in WSI classification. Extensive experiments on classification of the two widely-used datasets of TCGA-NSCLC and CAMELYON16 show CATTMIL’s effectiveness in suppressing the dataset bias and enhancing the generalization capability as well.

Cite this Paper

BibTeX
@InProceedings{pmlr-v260-wu25b, title = {Causal ATTention Multiple Instance Learning for Whole Slide Image Classification}, author = {Wu, Xiaochun and Wang, Haitao and Wu, Hejun}, booktitle = {Proceedings of the 16th Asian Conference on Machine Learning}, pages = {984--999}, year = {2025}, editor = {Nguyen, Vu and Lin, Hsuan-Tien}, volume = {260}, series = {Proceedings of Machine Learning Research}, month = {05--08 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v260/main/assets/wu25b/wu25b.pdf}, url = {https://proceedings.mlr.press/v260/wu25b.html}, abstract = {We propose a new multiple instance learning (MIL) method called Causal ATTention Multiple Instance Learning (CATTMIL) to alleviate the dataset bias for more accurate classification of whole slide images (WSIs). There are different kinds of dataset bias due to confounders that are rooted in data generation and/or pre-training dataset of MIL. Confounders might mislead MIL models to learn spurious correlations between instances and bag label. Such spurious correlations, in turn, impede the generalization ability of models and hurt the final performance. To fight against the negative impacts of confounders, CATTMIL exploits the causal intervention using the front-door adjustment with a Causal ATTention (CATT) mechanism. This enables CATTMIL to remove the spurious correlations so as to estimate the causal effect of instances on the bag label. Unlike previous deconfounded MIL methods, our CATTMIL does not need to approximate confounder values. Therefore, CATTMIL is able to bring further performance boosting to existing schemes and achieve the state-of-the-art in WSI classification. Extensive experiments on classification of the two widely-used datasets of TCGA-NSCLC and CAMELYON16 show CATTMIL’s effectiveness in suppressing the dataset bias and enhancing the generalization capability as well.} }
Endnote
%0 Conference Paper %T Causal ATTention Multiple Instance Learning for Whole Slide Image Classification %A Xiaochun Wu %A Haitao Wang %A Hejun Wu %B Proceedings of the 16th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Vu Nguyen %E Hsuan-Tien Lin %F pmlr-v260-wu25b %I PMLR %P 984--999 %U https://proceedings.mlr.press/v260/wu25b.html %V 260 %X We propose a new multiple instance learning (MIL) method called Causal ATTention Multiple Instance Learning (CATTMIL) to alleviate the dataset bias for more accurate classification of whole slide images (WSIs). There are different kinds of dataset bias due to confounders that are rooted in data generation and/or pre-training dataset of MIL. Confounders might mislead MIL models to learn spurious correlations between instances and bag label. Such spurious correlations, in turn, impede the generalization ability of models and hurt the final performance. To fight against the negative impacts of confounders, CATTMIL exploits the causal intervention using the front-door adjustment with a Causal ATTention (CATT) mechanism. This enables CATTMIL to remove the spurious correlations so as to estimate the causal effect of instances on the bag label. Unlike previous deconfounded MIL methods, our CATTMIL does not need to approximate confounder values. Therefore, CATTMIL is able to bring further performance boosting to existing schemes and achieve the state-of-the-art in WSI classification. Extensive experiments on classification of the two widely-used datasets of TCGA-NSCLC and CAMELYON16 show CATTMIL’s effectiveness in suppressing the dataset bias and enhancing the generalization capability as well.
APA
Wu, X., Wang, H. & Wu, H.. (2025). Causal ATTention Multiple Instance Learning for Whole Slide Image Classification. Proceedings of the 16th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 260:984-999 Available from https://proceedings.mlr.press/v260/wu25b.html.

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