Federated disentangled representation learning for unsupervised brain anomaly detection

With the advent of deep learning and increasing use brain MRIs, a great amount interest has arisen in automated anomaly segmentation to improve clinical workflows; however, it is time-consuming expensive curate medical imaging. Moreover, data are often scattered across many institutions, with privacy regulations hampering its use. Here we present FedDis collaboratively train an unsupervised convolutional autoencoder on 1,532 healthy magnetic resonance scans from four different evaluate performance identifying pathologies such as multiple sclerosis, vascular lesions, low- high-grade tumours/glioblastoma total 538 volumes six institutions. To mitigate statistical heterogeneity among disentangle parameter space into global (shape) local (appearance). Four institutes jointly shape parameters model anatomical structures. Every institute trains appearance locally allow for client-specific personalization domain-invariant features. We have shown that our collaborative approach, FedDis, improves results by 99.74% 83.33% lesions 40.45% tumours over trained models without need annotations or sharing private data. found out especially beneficial share both data, improving their up 227% sclerosis 77% tumours. Federated detection common techniques machine learning. The authors combine them, using multicentred datasets various diseases, automate abnormalities