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.
ArXiv. 2024 Jun 18*** epublish ***
Alex Chen, Nathan Lay, Stephanie Harmon, Kutsev Ozyoruk, Enis Yilmaz, Brad J Wood, Peter A Pinto, Peter L Choyke, Baris Turkbey