Endoscopic Image

Endoscopic image analysis uses computer vision techniques to improve the diagnosis and treatment of gastrointestinal diseases. Current research focuses on developing robust algorithms for tasks like depth estimation (often employing convolutional neural networks and transformers), image segmentation (leveraging techniques like style-content disentanglement and graph partitioning), and polyp detection, addressing challenges such as image artifacts, domain variations, and limited annotated data through self-supervised learning and data augmentation strategies. These advancements hold significant potential for improving the accuracy and efficiency of endoscopic procedures, leading to earlier disease detection and better patient outcomes.