  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 refined
pseudo labels, which were then used to train our location-based SSL model. We
show that our SSL method can improve prostate lesion detection by utilizing
unannotated images, with more substantial impacts being observed when larger
proportions of unannotated images are used.
