Anomaly Detection in Medical Images via Federated Machine Learning
Machine learning models can support physicians in the detection and characterization of anomalies (e.g., tumors) in medical images. Therefore, integrated and optimized structure recognition algorithms can add significant value to imaging devices. The recognition models can be markedly improved by training on diverse, large, and labelled image datasets. By using the Apheris Platform for federated and privacy preserving data science, machine learning models can be securely trained on datasets of various sources (e.g., hospitals) without sharing any patient data.