Cancer is a condition in which abnormal cells uncontrollably split and damage the body
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …

Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …

Early detection and assessment of polyps play a crucial role in the prevention and treatment
of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist …

Abstract Large Vision-Language Models (LVLMs) are capable of handling diverse data
types such as imaging, text, and physiological signals, and can be applied in various fields …

Analyzing medical data to find abnormalities is a time-consuming and costly task,
particularly for rare abnormalities, requiring tremendous efforts from medical experts …

Automatic surgical instrument segmentation is a necessary step for the steady operation of
surgical robots, and the segmentation accuracy directly affects the surgical effect …

Objective: One of the fundamental and crucial tasks for the automated diagnosis of
colorectal cancer is the segmentation of the acute gastrointestinal lesions, most commonly …

Medical image segmentation is a critical task in early disease detection and diagnosis. In
recent years, numerous variants of U-Net and Transformer-based models have …

Widely used traditional supervised deep learning methods require a large number of
training samples but often fail to generalize on unseen datasets. Therefore, a more general …

Gastrointestinal (GI) tract disorders are a significant public health issue. They are becoming
more common and can cause serious health problems and high healthcare costs. Small …