Work

Learning to segment with limited annotations: Self-supervised pretraining with Regression and Contrastive loss in MRI

Year: 2023

Type: conference-paper

Abstract: The availability of large unlabeled datasets compared to labeled ones motivate the use of self-supervised pretraining to initialize deep learning models for subsequent segmentation tasks. We consider two pre-training approaches for driving a CNN to learn different representations using: a) a reconstruction loss that exploits spatial dependencies and b) a contrastive loss that exploits semantic similarity. The techniques are evaluated in two MR segmentation applications: a) liver and b) prostate segmentation in T2-weighted images. We observed that CNNs pretrained using self-supervision can be finetuned for comparable performance with fewer labeled datasets.

Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition

DOI: 10.58530/2022/3914

Authors: Lavanya Umapathy, Zhiyang Fu, Rohit Philip, Diego Martin, Maria Altbach, Ali Bilgin

Institutions: University of Arizona, Houston Methodist

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Arizona Biomedical Research Commission, National Institutes of Health, National Cancer Institute