Work

A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images

Year: 2023

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2306.08955

Authors: Chen, Yanru, Lu, Michael T, Raghu, Vineet K

Institutions: Harvard University, Massachusetts General Hospital, Columbia University

Language: English

Topic: COVID-19 diagnosis using AI

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: American College of Radiology Imaging Network, National Cancer Institute

OpenAlex ID: W4380994170

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