LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908556918259712 |
|---|---|
| author | Wu, Shuyang Qiu, Yifu Nearchou, Ines P. Prost, Sandrine Fallowfield, Jonathan A. Ueno, Hideki Tsuda, Hitoshi Harrison, David J. Bilen, Hakan Kendall, Timothy J. |
| author_facet | Wu, Shuyang Qiu, Yifu Nearchou, Ines P. Prost, Sandrine Fallowfield, Jonathan A. Ueno, Hideki Tsuda, Hitoshi Harrison, David J. Bilen, Hakan Kendall, Timothy J. |
| contents | Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervision is typically provided only at the bag level, hindering the integrated consideration of instance and bag-level information during the analysis. In this work, we present LadderMIL, a framework designed to improve MIL through two perspectives: (1) employing instance-level supervision and (2) learning inter-instance contextual information at bag level. Firstly, we propose a novel Coarse-to-Fine Self-Distillation (CFSD) paradigm that probes and distils a network trained with bag-level information to adaptively obtain instance-level labels which could effectively provide the instance-level supervision for the same network in a self-improving way. Secondly, to capture inter-instance contextual information in WSI, we propose a Contextual Encoding Generator (CEG), which encodes the contextual appearance of instances within a bag. We also theoretically and empirically prove the instance-level learnability of CFSD. Our LadderMIL is evaluated on multiple clinically relevant benchmarking tasks including breast cancer receptor status classification, multi-class subtype classification, tumour classification, and prognosis prediction. Average improvements of 8.1%, 11% and 2.4% in AUC, F1-score, and C-index, respectively, are demonstrated across the five benchmarks, compared to the best baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation Wu, Shuyang Qiu, Yifu Nearchou, Ines P. Prost, Sandrine Fallowfield, Jonathan A. Ueno, Hideki Tsuda, Hitoshi Harrison, David J. Bilen, Hakan Kendall, Timothy J. Computer Vision and Pattern Recognition Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervision is typically provided only at the bag level, hindering the integrated consideration of instance and bag-level information during the analysis. In this work, we present LadderMIL, a framework designed to improve MIL through two perspectives: (1) employing instance-level supervision and (2) learning inter-instance contextual information at bag level. Firstly, we propose a novel Coarse-to-Fine Self-Distillation (CFSD) paradigm that probes and distils a network trained with bag-level information to adaptively obtain instance-level labels which could effectively provide the instance-level supervision for the same network in a self-improving way. Secondly, to capture inter-instance contextual information in WSI, we propose a Contextual Encoding Generator (CEG), which encodes the contextual appearance of instances within a bag. We also theoretically and empirically prove the instance-level learnability of CFSD. Our LadderMIL is evaluated on multiple clinically relevant benchmarking tasks including breast cancer receptor status classification, multi-class subtype classification, tumour classification, and prognosis prediction. Average improvements of 8.1%, 11% and 2.4% in AUC, F1-score, and C-index, respectively, are demonstrated across the five benchmarks, compared to the best baseline. |
| title | LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.02707 |