Search Results for author:
Found 5 papers, 1 papers with code
Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning
Results: Our results demonstrated that using baseline U-net yielded poorer lung segmentation results in our database than those in the JSRT and Montgomery databases, implying that robust segmentation of lungs may be difficult because of severe abnormalities.
Progressive Transformer-Based Generation of Radiology Reports
Inspired by Curriculum Learning, we propose a consecutive (i. e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two steps.
Boosting Radiology Report Generation by Infusing Comparison Prior
To tackle this issue, we propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports.
Radiology-Aware Model-Based Evaluation Metric for Report Generation
Furthermore, we demonstrate that one of our checkpoints exhibits a high correlation with human judgment, as assessed using the publicly available annotations of six board-certified radiologists, using a set of 200 reports.
