Quality monitoring of federated Covid-19 lesion segmentation
This addresses the challenge of quality monitoring in federated medical imaging for radiologists and healthcare providers, but it is incremental as it builds on existing federated learning methods.
The paper tackled the problem of monitoring segmentation quality in federated learning for Covid-19 lung lesions by developing lightweight metrics that detect over 70% of low-quality segmentations on out-of-distribution data, enabling reliable performance decline signals.
Federated Learning is the most promising way to train robust Deep Learning models for the segmentation of Covid-19-related findings in chest CTs. By learning in a decentralized fashion, heterogeneous data can be leveraged from a variety of sources and acquisition protocols whilst ensuring patient privacy. It is, however, crucial to continuously monitor the performance of the model. Yet when it comes to the segmentation of diffuse lung lesions, a quick visual inspection is not enough to assess the quality, and thorough monitoring of all network outputs by expert radiologists is not feasible. In this work, we present an array of lightweight metrics that can be calculated locally in each hospital and then aggregated for central monitoring of a federated system. Our linear model detects over 70% of low-quality segmentations on an out-of-distribution dataset and thus reliably signals a decline in model performance.