Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving property of FL algorithms makes them especially attractive to the medical field. However, in case of heterogeneous client data distributions, standard FL methods are unstable and require intensive hyperparameter tuning to achieve optimal performance. Conventional hyperparameter optimization algorithms are impractical in real-world FL applications as they involve numerous training trials, which are often not affordable with limited compute budgets. In this work, we propose an efficient reinforcement learning (RL)-based federated hyperparameter optimization algorithm, termed Auto-FedRL, in which an online RL agent can dynamically adjust hyperparameters of each client based on the current training progress. Extensive experiments are conducted to investigate different search strategies and RL agents. The effectiveness of the proposed method is validated on a heterogeneous data split of the CIFAR-10 dataset as well as two real-world medical image segmentation datasets for COVID-19 lesion segmentation in chest CT and pancreas segmentation in abdominal CT. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Camera-based distance detection and contact tracing to monitor potential spread of COVID-19

Purpose: This study aims to analyze a social distance monitoring and contact tracing assistance tool for preventing the spread of COVID-19 in a busy indoor working hospital environment. Method: A camera-based tool was developed. The tool estimates physical distance between multiple individuals in real-time and also tracks individuals and records their contact time when in violation social distance requirements for retrospect review. Both stereo- and monocular-camera tools are implemented and their accuracy and efficiency are evaluated and compared. Video was captured by a ZED M camera which was set close to the ceiling of a lab space. Three people within the field of view of the camera completed various movements. The distance (binary, <6 feet or >6 feet) and contact time between each pair was recorded as ground truth and compared to the video software analysis. Additionally, the contact time between any two individuals was calculated and compared to ground truth. Results: The overall accuracy of social distance detection was 95.1% and 74.4%, with a false-negative rate (when the tool predicts individuals are far enough apart, when they are actually too close) of 7.2% and 23.5% for the stereo and monocular tools, respectively. Conclusions: A stereo-camera social distance monitoring and contact tracing assistance tool can accurately detect social distance among multiple people, and keep an accurate contact record for each individual. While a monocular camera tool provided some level of certainty, a stereo camera tool was shown to be superior.