Joint semi-supervised and contrastive learning enables zero-shot domain-adaptation and multi-domain segmentation

arXiv:2405.05336v1 Announce Type: cross
Abstract: Despite their effectiveness, current deep learning models face challenges with images coming from different domains with varying appearance and content. We introduce SegCLR, a versatile framework designed to segment volumetric images across different domains, employing supervised and contrastive learning simultaneously to effectively learn from both labeled and unlabeled data. We demonstrate the superior performance of SegCLR through a comprehensive evaluation involving three diverse clinical datasets of retinal fluid segmentation in 3D Optical Coherence Tomography (OCT), …

abstract arxiv challenges cs.ai cs.cv current deep learning domain domains eess.iv face framework images learn segment segmentation semi semi-supervised type zero-shot