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 …

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it
combines information from various imaging modalities to provide a more comprehensive …

Since medical images include sensitive patient information, security is the top priority during
transmission. In addition to protecting patient data from potential criminals, security helps to …

Multimodal medical image fusion (MMIF) has been proven to effectively improve the
efficiency of disease diagnosis and treatment. However, few works have explored dedicated …

The main objective of image fusion for multimodal medical images is to retrieve valuable
information by combining multiple images obtained from various sources into a single image …

Recent studies on medical image fusion based on deep learning have made remarkable
progress, but the common and exclusive features of different modalities, especially their …

Multimodal medical image fusion (MMIF) is the process of merging different modalities of
medical images into a single output image (fused image) with a significant quantity of …

Semantic segmentation utilises the RGB-Thermal (RGB-T) source images with the capacity
of provide pixel-level prediction for surrounding scenes in harsh imaging conditions …

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the
diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation …

Multimodal medical image fusion is a perennially prominent research topic that can obtain
informative medical images and aid radiologists in diagnosing and treating disease more …