Search Results for author:
Found 3 papers, 1 papers with code
RRED : A Radiology Report Error Detector based on Deep Learning Framework
Specifically, our method detects errors between findings and conclusion of chest X-ray reports based on a supervised learning framework.
AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation
Although deep learning-based computer-aided diagnosis systems have recently achieved expert-level performance, developing a robust deep learning model requires large, high-quality data with manual annotation, which is expensive to obtain.
Learning Visual Context by Comparison
Current methods for solving this task exploit various characteristics of the chest X-ray image, but one of the most important characteristics is still missing: the necessity of comparison between related regions in an image.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
