Accurate segmentation of polyps is crucial for efficient colorectal cancer detection during the
colonoscopy screenings. State Space Models, exemplified by Mamba, have recently …

Captured colonoscopy videos usually suffer from multiple real-world distortions, such as
motion blur, low brightness, abnormal exposure, and object occlusion, which impede visual …

Polyp segmentation remains challenging for two reasons:(a) the size and shape of colon
polyps are variable and diverse;(b) the distinction between polyps and mucosa is not …

Colonoscopy is currently one of the most sensitive screening methods for colorectal cancer.
This study investigates the frontiers of intelligent colonoscopy techniques and their …

Objective Colorectal cancer (CRC) accounts for a significant number of deaths around the
globe, with 1.2 million expected deaths by 2030, making it one of the major concerns in …

Foundational vision models, such as the Segment Anything Model (SAM), have achieved
significant breakthroughs through extensive pre-training on large-scale visual datasets …

Automatic segmentation of polyps from colonoscopy images plays a critical role in early
screening and treatment of colorectal cancer. Although deep learning methods have made …

The current work introduces the hybrid ensemble framework for the detection and
segmentation of colorectal cancer. This framework will incorporate both supervised …

In the field of medical imaging, the automatic detection and segmentation of colon polyps is
particularly crucial for the early diagnosis of colorectal cancer. However, existing methods …