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.

本文提出了一种名为 FedCy 的联邦半监督学习方法，该方法结合了 FL 和自监督学习，利用带标签和未标记视频的分散式数据集，提高了手术分阶段识别任务的性能。我们证明了与最先进的 FSSL 方法相比，在新采集的多机构数据集上对于腹腔镜胆囊切除术的手术阶段自动识别任务有明显的性能提高，并且当测试数据来自于看不见领域时，该方法还学会了更具有一般性的特征。