6 accepted papers
Automatic medical report generation has the potential to support clinical diagnosis, reduce the workload of radiologists, and demonstrate potential for enhancing diagnostic consistency. However, current evaluation metrics often fail to reflect the clinical reliability of generated reports. Overlap-b
Deep unrolling models (DUMs) have shown great poten-tial in sparse-view CT reconstruction by combining itera-tive optimization and deep learning. However, most DUMsinsufficiently account for physical degradation from sparse-view imaging, leading to slow convergence and persistentartifacts. To addres
Pose-agnostic Anomaly Detection (PAD) aims to detect anomalies when the poses of query images are unknown and differ from those in the training set. Therefore, accurately estimating the camera poses for the query images in the test set is critical for this task. Existing query-specific framework met
Accurate segmentation of airways in Low-Resolution CT (LRCT) scans is vital for diagnostics in scenarios such as reduced radiation exposure, emergency response, or limited resources. Yet manual annotation is labor-intensive and prone to variability, while existing automated methods often fail to cap
Despite significant advancements in automated report generation, the opaqueness of text interpretability continues to cast doubt on the reliability of the content produced. This paper introduces a novel approach to identify specific image features in X-ray images that influence the outputs of report
Blind deblurring has attracted much interest with its wide applications in reality. The blind deblurring problem is usually solved by estimating the intermediate kernel and the intermediate image alternatively, which will finally converge to the blurring kernel of the observed image. Numerous works…