Performance changes in automated lesion detection under federated learning with sequential institution addition.

PURPOSE: Federated learning (FL) enables multiple institutions to collaboratively train machine learning models while keeping data local and has attracted attention in medical image processing, including computer-aided detection (CAD). In FL, performance is expected to improve through retraining as additional institutions participate. The purpose of this study was to investigate how CAD software performance changes as the number of participating institutions is sequentially increased within an FL framework. METHODS: We used two types of CAD software for cerebral aneurysm detection in magnetic resonance (MR) angiography images and brain metastasis detection in contrast-enhanced T1-weighted MR images. Datasets from different institutions or scanner vendors were treated as independent FL clients and incorporated sequentially. Training strategies included from-scratch training, fine-tuning (FT) of selected layers, and full fine-tuning (FFT) of all parameters. Performance was assessed using the competition performance metric on test sets from the initial institutions as well as from all participating institutions. RESULTS: For both CAD software types, sequential institution addition combined with FT generally showed higher median performance changes than from-scratch training. FT showed performance comparable to that of FFT while requiring substantially fewer trainable parameters. Performance improvements generally accumulated with sequential institution addition, whereas simultaneous addition resulted in less consistent improvements. CONCLUSIONS: Sequential institution addition under FL may improve CAD software performance when combined with appropriate FT strategies. FT that updates only a subset of layers may achieve performance changes comparable to those of FFT while requiring substantially fewer trainable parameters across different lesion detection tasks.