2024-11-17-saada24a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images	Proceedings of the MICCAI Workshop on Computational Pathology	Multiple Instance Learning (MIL) models have proven effective for cancer prognosis from Whole Slide Images. However, the original MIL formulation incorrectly assumes the patches of the same image to be independent, leading to a loss of spatial context as information flows through the network. Incorporating contextual knowledge into predictions is particularly important given the inclination for cancerous cells to form clusters and the presence of spatial indicators for tumors. State-of-the-art methods often use attention mechanisms eventually combined with graphs to capture spatial knowledge. In this paper, we take a novel and transversal approach, addressing this issue through the lens of regularization. We propose Context-Aware Regularization for Multiple Instance Learning (CARMIL), a versatile regularization scheme designed to seamlessly integrate spatial knowledge into any MIL model. Additionally, we present a new and generic metric to quantify the Context- Awareness of any MIL model when applied to Whole Slide Images, resolving a previously unexplored gap in the field. The efficacy of our framework is evaluated for two survival analysis tasks on glioblastoma (TCGA GBM) and colon cancer data (TCGA COAD).	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	saada24a	0	CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images	154	169	154-169	154	false	Saada, Thiziri Nait and Di-Proietto, Valentina and Schmauch, Benoit and Loga, Katharina Von and Fidon, Lucas	given family Thiziri Nait Saada given family Valentina Di-Proietto given family Benoit Schmauch given family Katharina Von Loga given family Lucas Fidon	2024-11-17		Proceedings of the MICCAI Workshop on Computational Pathology	254	inproceedings	date-parts 2024 11 17	https://raw.githubusercontent.com/mlresearch/v254/main/assets/saada24a/saada24a.pdf