Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection

arXiv:2406.12177v1 Announce Type: cross
Abstract: Prostate cancer is one of the most prevalent malignancies in the world. While deep learning has potential to further improve computer-aided prostate cancer detection on MRI, its efficacy hinges on the exhaustive curation of manually annotated images. We propose a novel methodology of semisupervised learning (SSL) guided by automatically extracted clinical information, specifically the lesion locations in radiology reports, allowing for use of unannotated images to reduce the annotation burden. By leveraging lesion locations, we …

abstract arxiv cancer cancer detection computer cs.cv cs.lg curation deep learning detection images location methodology mri novel potential prostate cancer radiology report semi semi-supervised semi-supervised learning ssl supervised learning type while world