Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease

• URL: http://arxiv.org/abs/2107.03901v1
• Date: Wed, 7 Jul 2021 08:54:08 GMT
• Title: Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease
• Authors: Akis Linardos, Kaisar Kushibar, Sean Walsh, Polyxeni Gkontra, Karim Lekadir
• Abstract summary: We present the first federated learning study on the modality of cardiovascular magnetic resonance (CMR) We use four centers derived from subsets of the M&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy (HCM) We show that despite the small size of data (180 subjects derived from four centers), the privacy preserving federated learning achieves promising results.
• Score: 0.8687046723936027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models can enable accurate and efficient disease diagnosis, but have thus far been hampered by the data scarcity present in the medical world. Automated diagnosis studies have been constrained by underpowered single-center datasets, and although some results have shown promise, their generalizability to other institutions remains questionable as the data heterogeneity between institutions is not taken into account. By allowing models to be trained in a distributed manner that preserves patients' privacy, federated learning promises to alleviate these issues, by enabling diligent multi-center studies. We present the first federated learning study on the modality of cardiovascular magnetic resonance (CMR) and use four centers derived from subsets of the M\&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy (HCM). We adapt a 3D-CNN network pretrained on action recognition and explore two different ways of incorporating shape prior information to the model, and four different data augmentation set-ups, systematically analyzing their impact on the different collaborative learning choices. We show that despite the small size of data (180 subjects derived from four centers), the privacy preserving federated learning achieves promising results that are competitive with traditional centralized learning. We further find that federatively trained models exhibit increased robustness and are more sensitive to domain shift effects.

Related papers

• Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures [0.8132630541462695]
  In this study, we leverage federated learning strategies to address clopidogrel treatment failure detection. We partitioned the data based on geographic centers and evaluated the performance of federated learning. Our findings underscore the potential of federated learning in addressing clopidogrel treatment failure detection.
  arXiv  Detail & Related papers  (2024-03-05T23:31:07Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Decentralized Distributed Learning with Privacy-Preserving Data Synthesis [9.276097219140073]
  In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. Recent privacy regulations hinder the possibility to share data, and consequently, to come up with machine learning-based solutions that support diagnosis and prognosis. We present a decentralized distributed method that integrates features from local nodes, providing models able to generalize across multiple datasets while maintaining privacy.
  arXiv  Detail & Related papers  (2022-06-20T23:49:38Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation [2.294014185517203]
  We focus on the federated learning paradigm, a distributed learning approach for decentralized data. Studies show that federated learning can provide competitive performance with conventional central training. We explore different strategies for faster convergence and better performance which can also work on strong Non-IID cases.
  arXiv  Detail & Related papers  (2022-02-16T07:49:04Z)
• 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)
• FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
  COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19. The data itself is still scarce due to patient privacy concerns. We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
  arXiv  Detail & Related papers  (2021-02-10T01:56:58Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)
• 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.