Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Accurate medical image segmentation is of great significance for computer aided diagnosis.
Although methods based on convolutional neural networks (CNNs) have achieved good …

Characteristics such as low contrast and significant organ shape variations are often
exhibited in medical images. The improvement of segmentation performance in medical …

Building trustworthy and transparent image-based medical artificial intelligence (AI) systems
requires the ability to interrogate data and models at all stages of the development pipeline …

Abstract Large Vision-Language Models (LVLMs) have demonstrated remarkable
capabilities in various multimodal tasks. However their potential in the medical domain …

The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …

Abstract Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual
representations with promising zero-shot performance. To further improve its downstream …

Human Skin cancer is commonly detected visually through clinical screening followed by a
dermoscopic examination. However, automated skin lesion classification remains …

Abstract The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion
analysis is an emerging field of research that has the potential to alleviate the burden and …