Federated Multi-organ Segmentation with Inconsistent Labels
- URL: http://arxiv.org/abs/2206.07156v2
- Date: Thu, 25 May 2023 15:34:35 GMT
- Title: Federated Multi-organ Segmentation with Inconsistent Labels
- Authors: Xuanang Xu, Hannah H. Deng, Jaime Gateno, Pingkun Yan
- Abstract summary: Federated learning is an emerging paradigm allowing large-scale decentralized learning without sharing data across different data owners.
In practice, each clinical site may only annotate certain organs of interest with partial or no overlap with other sites.
This work tackles the problem by using a novel federated multi-encoding U-Net (Fed-MENU) method for multi-organ segmentation.
- Score: 14.096407787470701
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Federated learning is an emerging paradigm allowing large-scale decentralized
learning without sharing data across different data owners, which helps address
the concern of data privacy in medical image analysis. However, the requirement
for label consistency across clients by the existing methods largely narrows
its application scope. In practice, each clinical site may only annotate
certain organs of interest with partial or no overlap with other sites.
Incorporating such partially labeled data into a unified federation is an
unexplored problem with clinical significance and urgency. This work tackles
the challenge by using a novel federated multi-encoding U-Net (Fed-MENU) method
for multi-organ segmentation. In our method, a multi-encoding U-Net (MENU-Net)
is proposed to extract organ-specific features through different encoding
sub-networks. Each sub-network can be seen as an expert of a specific organ and
trained for that client. Moreover, to encourage the organ-specific features
extracted by different sub-networks to be informative and distinctive, we
regularize the training of the MENU-Net by designing an auxiliary generic
decoder (AGD). Extensive experiments on six public abdominal CT datasets show
that our Fed-MENU method can effectively obtain a federated learning model
using the partially labeled datasets with superior performance to other models
trained by either localized or centralized learning methods. Source code is
publicly available at https://github.com/DIAL-RPI/Fed-MENU.
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