2024-01-23-chen24c.md

title	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
A comparison of self-supervised pretraining approaches for predicting disease risk from chest radiograph images	Deep learning is the state-of-the-art for medical imaging tasks, but requires large, labeled datasets. For risk prediction, large datasets are rare since they require both imaging and follow-up (e.g., diagnosis codes). However, the release of publicly available imaging data with diagnostic labels presents an opportunity for self and semi-supervised approaches to improve label efficiency for risk prediction. Though several studies have compared self-supervised approaches in natural image classification, object detection, and medical image interpretation, there is limited data on which approaches learn robust representations for risk prediction. We present a comparison of semi- and self-supervised learning to predict mortality risk using chest x-ray images. We find that a semi-supervised autoencoder outperforms contrastive and transfer learning in internal and external validation.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chen24c	0	A comparison of self-supervised pretraining approaches for predicting disease risk from chest radiograph images	1826	1858	1826-1858	1826	false	Chen, Yanru and Lu, Michael T and Raghu, Vineet K	given family Yanru Chen given family Michael T Lu given family Vineet K Raghu	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/chen24c/chen24c.pdf