2024-11-17-chen24a.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
Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective	Proceedings of the MICCAI Workshop on Computational Pathology	Recent advances in artificial intelligence (AI), in particular self-supervised learning of foundation models (FMs), are revolutionizing medical imaging and computational pathology (CPath). A constant challenge in the analysis of digital Whole Slide Images (WSIs) is the problem of aggregating tens of thousands of tile-level image embeddings to a slide-level representation. Due to the prevalent use of datasets created for genomic research, such as TCGA, for method development, the performance of these techniques on diagnostic slides from clinical practice has been inadequately explored. This study conducts a thorough benchmarking analysis of ten slide-level aggregation techniques across nine clinically relevant tasks, including diagnostic assessment, biomarker classification, and outcome prediction. The results yield following key insights: (1) Embeddings derived from domainspecific (histological images) FMs outperform those from generic ImageNet-based models across aggregation methods. (2) Spatial-aware aggregators enhance the performance significantly when using ImageNet pre-trained models but not when using FMs. (3) No single model excels in all tasks and spatially-aware models do not show general superiority as it would be expected. These findings underscore the need for more adaptable and universally applicable aggregation techniques, guiding future research towards tools that better meet the evolving needs of clinical-AI in pathology. The code used in this work are available at https://github.com/fuchs-lab-public/CPath_SABenchmark	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chen24a	0	Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective	38	50	38-50	38	false	Chen, Shengjia and Campanella, Gabriele and Elmas, Abdulkadir and Stock, Aryeh and Zeng, Jennifer and Polydorides, Alexandros D. and Schoenfeld, Adam J. and Huang, Kuan-lin and Houldsworth, Jane and Vanderbilt, Chad and Fuchs, Thomas J.	given family Shengjia Chen given family Gabriele Campanella given family Abdulkadir Elmas given family Aryeh Stock given family Jennifer Zeng given family Alexandros D. Polydorides given family Adam J. Schoenfeld given family Kuan-lin Huang given family Jane Houldsworth given family Chad Vanderbilt given family Thomas J. Fuchs	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/chen24a/chen24a.pdf