4 accepted papers
This study addresses two key challenges in structuring radiology reports: the lack of a practical structuring schema and datasets to evaluate model generalizability. To address these challenges, we propose a “Finding-Centric Structuring,” which organizes reports around individual findings, facilitat…
Radiology report generation systems have the potential to reduce the workload of radiologists by automatically describing the findings in medical images.To broaden the application of the report generation system, the system should generate reports that are not only factually accurate but also chrono…
Automatic segmentation of COVID-19 lesions is essential for computer-aided diagnosis. However, this task remains challenging because widely-used supervised based methods require large-scale annotated data that is difficult to obtain. Although an unsupervised method based on anomaly detection has sho…
Deep network based methods have been proposed for accurate classification of pulmonary textures on CT images. However, such methods well-trained on CT data from one scanner cannot perform well when they are directly applied to the data from other scanners. This domain shift problem is caused by diff…