The classification of medical images is crucial in the biomedical field, and despite attempts
to address the issue, significant challenges persist. To effectively categorize medical …

Multi-modal medical image fusion aims to integrate distinct imaging modalities to yield more
comprehensive and precise medical images, which can benefit the subsequent image …

Medical vision-language pre-training (Med-VLP) models have recently accelerated the fast-
growing medical diagnostics application. However, most Med-VLP models learn task …

For accurate segmentation, effective feature extraction has always been a challenging
problem, since the variability of appearance and the fuzziness of object boundaries …

Image fusion integrates essential information from multiple source images into a single
composite, emphasizing the highlighting structure and textures, and refining imperfect areas …

Abstract Magnetic Resonance Imaging (MRI) is a crucial tool for quantitative image analysis
and clinical diagnosis, providing detailed anatomical images to assist in the detection of …

Image quality assessment (IQA) and its computational models play a vital role in modern
computer vision applications. Research has traditionally focused on signal distortions …

Interpretable and robust medical diagnoses are essential traits for practicing clinicians. Most
computer-augmented diagnostic systems suffer from three major problems: non …

Deep learning (DL)-based Low-dose CT (LDCT) image denoising methods may face
domain shift problem, where data from different domains (ie, hospitals) may have similar …

Image fusion is a classical problem in the field of image processing whose solutions are
usually not unique. The common image fusion methods can only generate a fixed fusion …