The number of studies on deep learning for medical diagnosis is expanding, and these
systems are often claimed to outperform clinicians. However, only a few systems have …

Skip connections are fundamental architecture designs for modern deep neural networks
(DNNs) such as CNNs and ViTs. While they help improve model performance significantly …

The computer-aided diagnosis system for esophageal cancer (EC) holds vital significance in
EC diagnosis and treatment making, with a primary focus on accurate segmentation of EC …

Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose
diagnostic and assessment challenges. Skin image analysis is a promising noninvasive …

Addressing the challenges of rare diseases is difficult, especially with the limited number of
reference images and a small patient population. This is more evident in rare skin diseases …

Medical image segmentation using deep neural networks has been highly successful.
However, the effectiveness of these networks is often limited by inadequate dense prediction …

Multi-input models for image classification have recently gained considerable attention.
However, multi-input models do not always exhibit superior performance compared to single …

Automated classification of skin lesions in dermoscopy images has the potential to
significantly improve survival rates and reduce the risk of death for skin cancer patients …

The Segment Anything Model (SAM) exhibits impressive capabilities in zero-shot
segmentation for natural images. Recently, SAM has gained a great deal of attention for its …

Vision-language pre-training (VLP) leverages easily accessible image-text pairs instead of
high-cost expert-annotated labels for pre-training, which has achieved promising …