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

TL;DR

This paper introduces a semi-supervised learning approach for prostate cancer detection on MRI that leverages automatically extracted lesion locations from radiology reports to improve detection accuracy while reducing the need for manual annotations.

Contribution

The study presents a novel location-guided semi-supervised learning method that refines pseudo labels using clinical report information, enhancing prostate lesion detection with fewer annotated images.

Findings

Improved prostate lesion detection accuracy using unannotated images.

Greater benefits observed with larger proportions of unannotated data.

Effective use of clinical report data to guide semi-supervised learning.

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 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.