Y Modular Self-Supervised Learning for Hand Surgical Diagnosis

Hand is a formidable and very complex organ. Each anatomical structure, even the smallest, can play a crucial role: arteries, tendons, nerves, work together to generate a large range of movements. Therefore, hand surgery remains a complex operation, and requires a lot of skills and experience. In this paper, we explore two different object detection problems linked to hand surgery: The detection of anatomical structures and lesions, using modular machine learning, and the Endoscopic Carpal Tunnel Release, with the help of Self-Supervised Learning. We use object detection to detect anatomical structures, and lesions. We show that modular machine learning and SelfSupervised Learning can be used to improve the results, even with only a small amount of labeled data.