Polyp Detection

Polyp detection in colonoscopy aims to automate the identification of polyps, precancerous growths in the colon, to improve the accuracy and efficiency of colorectal cancer screening. Current research heavily utilizes deep learning, employing architectures like YOLO, U-Net, Vision Transformers, and various convolutional neural networks, often incorporating techniques such as contrastive learning, attention mechanisms, and self-supervised learning to enhance accuracy and generalization across diverse datasets and imaging modalities. Improved polyp detection through these AI-driven methods has the potential to significantly reduce colorectal cancer mortality by increasing the rate of early diagnosis and facilitating timely intervention.