Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

Highly imbalanced datasets are ubiquitous in medical image classification problems. In such
problems, it is often the case that rare classes associated to less prevalent diseases are …

Colonoscopy is the gold standard for examination and detection of colorectal polyps.
Localization and delineation of polyps can play a vital role in treatment (eg, surgical …

Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and
be helpful to assess lesions more accurately. To this extent, semantic segmentation methods …

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 …

This review paper presents an in-depth exploration of gastrointestinal (GI) tract image
analysis, particularly emphasizing organ and polyp segmentation. It addresses the inherent …

Colorectal cancer is a prevalent disease in modern times, with most cases being caused by
polyps. Therefore, the segmentation of polyps has garnered significant attention in the field …

Colorectal cancer (CRC) is the third most common cause of cancer-related deaths in the
United States and is anticipated to cause another 52,580 deaths in 2023. The standard …

We study the impact of different loss functions on lesion segmentation from medical images.
Although the Cross-Entropy (CE) loss is the most popular option when dealing with natural …

With the development of biomedical computing, the segmentation task is integral in hel**
the doctor correctly identify the position of the polyps or the ache in the system. However …