Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures

TL;DR

This paper introduces a federated learning approach combined with AutoML for personalized prostate MRI segmentation, enabling local adaptation of neural architectures to improve performance without sharing sensitive data.

Contribution

It proposes a novel federated AutoML method with personalized neural architectures for prostate MRI segmentation, addressing data heterogeneity and privacy concerns.

Findings

Improved segmentation accuracy after local adaptation.

Effective personalization of models across diverse datasets.

Demonstrated robustness across multiple MRI datasets.

Abstract

Building robust deep learning-based models requires diverse training data, ideally from several sources. However, these datasets cannot be combined easily because of patient privacy concerns or regulatory hurdles, especially if medical data is involved. Federated learning (FL) is a way to train machine learning models without the need for centralized datasets. Each FL client trains on their local data while only sharing model parameters with a global server that aggregates the parameters from all clients. At the same time, each client's data can exhibit differences and inconsistencies due to the local variation in the patient population, imaging equipment, and acquisition protocols. Hence, the federated learned models should be able to adapt to the local particularities of a client's data. In this work, we combine FL with an AutoML technique based on local neural architecture search by training a "supernet". Furthermore, we propose an adaptation scheme to allow for personalized model architectures at each FL client's site. The proposed method is evaluated on four different datasets from 3D prostate MRI and shown to improve the local models' performance after adaptation through selecting an optimal path through the AutoML supernet.