Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation

arXiv:2311.10696v2 Announce Type: replace
Abstract: A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance. However, building such a model typically demands a large, diverse, and fully annotated dataset, which is challenging to obtain due to the labor-intensive nature of data curation. To address this challenge, we propose a cost-effective alternative that harnesses multi-source data with only partial or sparse segmentation labels for training, substantially reducing the cost of …

abstract acquired arxiv building cs.cv dataset datasets deployment diverse equipment however image images labor maintenance medical model deployment segmentation type via