FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation

• URL: http://arxiv.org/abs/2103.03705v1
• Date: Fri, 5 Mar 2021 14:29:52 GMT
• Title: FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
• Authors: Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert and Shadi Albarqouni
• Abstract summary: We propose Federated Disentanglement (FedDis) to disentangle the parameter space into shape and appearance. FedDis is based on the assumption that the anatomical structure in brain MRI images is similar across multiple institutions. We demonstrate a superior performance of FedDis on real pathological databases containing 109 subjects.
• Score: 12.863361425822314
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, data-driven machine learning (ML) methods have revolutionized the computer vision community by providing novel efficient solutions to many unsolved (medical) image analysis problems. However, due to the increasing privacy concerns and data fragmentation on many different sites, existing medical data are not fully utilized, thus limiting the potential of ML. Federated learning (FL) enables multiple parties to collaboratively train a ML model without exchanging local data. However, data heterogeneity (non-IID) among the distributed clients is yet a challenge. To this end, we propose a novel federated method, denoted Federated Disentanglement (FedDis), to disentangle the parameter space into shape and appearance, and only share the shape parameter with the clients. FedDis is based on the assumption that the anatomical structure in brain MRI images is similar across multiple institutions, and sharing the shape knowledge would be beneficial in anomaly detection. In this paper, we leverage healthy brain scans of 623 subjects from multiple sites with real data (OASIS, ADNI) in a privacy-preserving fashion to learn a model of normal anatomy, that allows to segment abnormal structures. We demonstrate a superior performance of FedDis on real pathological databases containing 109 subjects; two publicly available MS Lesions (MSLUB, MSISBI), and an in-house database with MS and Glioblastoma (MSI and GBI). FedDis achieved an average dice performance of 0.38, outperforming the state-of-the-art (SOTA) auto-encoder by 42% and the SOTA federated method by 11%. Further, we illustrate that FedDis learns a shape embedding that is orthogonal to the appearance and consistent under different intensity augmentations.

Related papers

• Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities [5.629645463085369]
  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. We show that Federated Learning framework can train a single model that is shown to be very promising in segmenting all disease types seen during training.
  arXiv  Detail & Related papers  (2024-06-17T15:16:18Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks [9.873532358701803]
  We propose a federated joint estimator, NOTEARS-PFL, for simultaneous learning of multiple Bayesian networks. We evaluate the performance of the proposed method on both synthetic and real-world multi-site rs-fMRI datasets.
  arXiv  Detail & Related papers  (2023-01-06T08:58:06Z)
• FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation [9.203390025029883]
  liver segmentation is one of the most common methods for analyzing CT and MRI images for diagnosis and follow-up treatment. Recent advances in deep learning have demonstrated encouraging results for automatic liver segmentation. Federated Learning has been proposed as a solution to alleviate challenges by training a shared global model on distributed clients. We propose Fednorm and its extension fednormp, two Federated Learning algorithms that use a modality-based normalization technique.
  arXiv  Detail & Related papers  (2022-05-23T07:34:34Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis [55.939957482776194]
  We propose a new benchmark for federated domain translation on unsupervised brain image synthesis (termed as FedMed-GAN) FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods.
  arXiv  Detail & Related papers  (2022-01-22T02:50:29Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)
• Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results [13.615292855384729]
  To train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. Due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data.
  arXiv  Detail & Related papers  (2020-01-16T04:49:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.