Learning Anatomically Consistent Embedding for Chest Radiography

arXiv:2312.00335v2 Announce Type: replace
Abstract: Self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated images. Compared with photographic images, medical images acquired with the same imaging protocol exhibit high consistency in anatomy. To exploit this anatomical consistency, this paper introduces a novel SSL approach, called PEAC (patch embedding of anatomical consistency), for medical image analysis. Specifically, in this paper, we propose to learn global and local consistencies via stable grid-based matching, transfer pre-trained PEAC …

abstract acquired arxiv consistent cs.cv embedding exploit images imaging medical novel paper patch protocol replace self-supervised learning ssl success supervised learning type visual