Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics

• URL: http://arxiv.org/abs/2308.11027v1
• Date: Mon, 21 Aug 2023 20:30:51 GMT
• Title: Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
• Authors: Zhuohang Li, Chao Yan, Xinmeng Zhang, Gharib Gharibi, Zhijun Yin, Xiaoqian Jiang, Bradley A. Malin
• Abstract summary: We show how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning.
• Score: 20.72616921953282
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks.

Related papers

• Improving the Classification Effect of Clinical Images of Diseases for Multi-Source Privacy Protection [0.0]
  Privacy data protection in the medical field poses challenges to data sharing. Traditional centralized training methods are difficult to apply due to violations of privacy protection principles. We propose a medical privacy data training framework based on data vectors.
  arXiv  Detail & Related papers  (2024-08-23T12:52:24Z)
• 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)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• 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)
• Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor [55.9023096444383]
  Current deep learning models are characterised by catastrophic forgetting of old knowledge when learning new classes. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning.
  arXiv  Detail & Related papers  (2022-04-28T08:41:51Z)
• LifeLonger: A Benchmark for Continual Disease Classification [59.13735398630546]
  We introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch. Cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
  arXiv  Detail & Related papers  (2022-04-12T12:25:05Z)
• 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)
• 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)
• Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease [0.8687046723936027]
  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.
  arXiv  Detail & Related papers  (2021-07-07T08:54:08Z)
• 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)
• Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning [9.727026678755678]
  We study the use of federated learning between two institutions in a real-world setting to collaboratively train a model. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training.
  arXiv  Detail & Related papers  (2020-09-28T08:54:10Z)

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.