Surgical Datasets

Surgical datasets are crucial for training and evaluating computer vision models aimed at improving minimally invasive surgery. Current research focuses on creating and improving these datasets, addressing limitations like insufficient size, annotation inconsistencies, and lack of diversity across procedures and institutions, with a particular emphasis on developing robust methods for handling noisy data and large tissue deformations. Researchers are employing various deep learning architectures, including Vision Transformers, convolutional neural networks, and recurrent neural networks, often combined with techniques like self-supervised learning and domain adaptation to overcome data scarcity. The development of high-quality, diverse surgical datasets is essential for advancing the field of surgical computer vision and ultimately improving patient outcomes through more precise and efficient surgical procedures.