2024-11-17-robbins24a.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
Prediction of KRAS mutation status from H&E foundation model embeddings in non-small cell lung cancer	Proceedings of the MICCAI Workshop on Computational Pathology	We predicted KRAS mutation status on non-small cell lung cancer (NSCLC) H&E images from foundation model embeddings. We evaluated a variety of attention-based multiple instance learning (MIL) models and aggregation strategies for a tilewise linear classifier. MIL with self-attention performed the best (AUC=0.822) followed by the minimum over tiles classified with the linear model (AUC=0.810). Self-attention was necessary for MIL to surpass tilewise linear classification when a wide range of aggregation techniques was considered.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	robbins24a	0	Prediction of {KRAS} mutation status from H&E foundation model embeddings in non-small cell lung cancer	170	179	170-179	170	false	Robbins, Marc and Loo, Jessica and Vyawahare, Saurabh and Wang, Yang Von and Mcneil, Carson and Steiner, Dave and Rao, Sudha and Wong, Pok Fai and Rivlin, Ehud and Weaver, Shamira and Goldenberg, Roman	given family Marc Robbins given family Jessica Loo given family Saurabh Vyawahare given family Yang Von Wang given family Carson Mcneil given family Dave Steiner given family Sudha Rao given family Pok Fai Wong given family Ehud Rivlin given family Shamira Weaver given family Roman Goldenberg	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/robbins24a/robbins24a.pdf