Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities
CoRR(2024)
Abstract
Segmentation models for brain lesions in MRI are commonly developed for a
specific disease and trained on data with a predefined set of MRI modalities.
Each such model cannot segment the disease using data with a different set of
MRI modalities, nor can it segment any other type of disease. Moreover, this
training paradigm does not allow a model to benefit from learning from
heterogeneous databases that may contain scans and segmentation labels for
different types of brain pathologies and diverse sets of MRI modalities.
Additionally, the sensitivity of patient data often prevents centrally
aggregating data, necessitating a decentralized approach. Is it feasible to use
Federated Learning (FL) to train a single model on client databases that
contain scans and labels of different brain pathologies and diverse sets of MRI
modalities? We demonstrate promising results by combining appropriate, simple,
and practical modifications to the model and training strategy: Designing a
model with input channels that cover the whole set of modalities available
across clients, training with random modality drop, and exploring the effects
of feature normalization methods. Evaluation on 7 brain MRI databases with 5
different diseases shows that such FL framework can train a single model that
is shown to be very promising in segmenting all disease types seen during
training. Importantly, it is able to segment these diseases in new databases
that contain sets of modalities different from those in training clients. These
results demonstrate, for the first time, the feasibility and effectiveness of
using Federated Learning to train a single 3D segmentation model on
decentralised data with diverse brain diseases and MRI modalities, a necessary
step towards leveraging heterogeneous real-world databases. Code will be made
available at: https://github.com/FelixWag/FL-MultiDisease-MRI
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined