Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases
This work addresses data annotation bottlenecks and privacy concerns in medical AI for surgical phase recognition, offering an incremental improvement over existing federated semi-supervised learning methods.
The paper tackles the problem of limited labeled data and data privacy in surgical phase recognition by proposing FedCy, a federated semi-supervised learning method that combines federated learning and self-supervised learning, achieving significant performance gains over state-of-the-art methods and learning more generalizable features for unseen domains.
Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering the sensitive nature of medical data. Recently proposed collaborative learning methods such as Federated Learning (FL) allow for training on remote datasets without the need to explicitly share data. Even so, data annotation still represents a bottleneck, particularly in medicine and surgery where clinical expertise is often required. With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition. By leveraging temporal patterns in the labeled data, FedCy helps guide unsupervised training on unlabeled data towards learning task-specific features for phase recognition. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases using a newly collected multi-institutional dataset of laparoscopic cholecystectomy videos. Furthermore, we demonstrate that our approach also learns more generalizable features when tested on data from an unseen domain.