  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.
