Colorectal Cancer Polyp

Colorectal cancer polyps, small growths in the colon, are crucial to detect early as they can become cancerous. Current research heavily focuses on automated polyp detection and segmentation in colonoscopy images using deep learning, employing architectures like U-Net, Faster R-CNN, and transformer-based models, often enhanced with techniques like Bayesian methods for uncertainty quantification and self-supervised learning for improved generalization. These advancements aim to improve the accuracy and efficiency of polyp detection, potentially reducing the high miss rate in clinical practice and leading to earlier diagnosis and treatment of colorectal cancer. The development of robust, real-time systems, including those incorporating tactile sensing for improved characterization, is a key area of ongoing investigation.