Summary of Location-based Radiology Report-guided Semi-supervised Learning For Prostate Cancer Detection, by Alex Chen et al.
Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection
by Alex Chen, Nathan Lay, Stephanie Harmon, Kutsev Ozyoruk, Enis Yilmaz, Brad J. Wood, Peter A. Pinto, Peter L. Choyke, Baris Turkbey
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel methodology for improving computer-aided prostate cancer detection on MRI using deep learning and semisupervised learning (SSL). The approach leverages automatically extracted clinical information from radiology reports, specifically lesion locations, to refine pseudo labels and train an SSL model. This method allows for the use of unannotated images, reducing the annotation burden and improving detection accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Prostate cancer is a common type of cancer that affects many people worldwide. Researchers are working on new ways to detect it using computers and medical images. This paper suggests a new approach that uses special computer learning tools to help find prostate lesions on MRI scans. The method works by looking at information from doctors’ reports, like where the lesions are located, to make better predictions about what’s in the image. |
Keywords
* Artificial intelligence * Deep learning
