Split Learning for collaborative deep learning in healthcare

• URL: http://arxiv.org/abs/1912.12115v1
• Date: Fri, 27 Dec 2019 14:39:58 GMT
• Title: Split Learning for collaborative deep learning in healthcare
• Authors: Maarten G.Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar
• Abstract summary: Shortage of labeled data has been holding the surge of deep learning in healthcare back. We argue for a split learning approach and apply this distributed learning method for the first time in the medical field.
• Score: 12.128894212674917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-center collaborative studies are a burden to set up. Distributed machine learning methods promise to mitigate these problems. We argue for a split learning based approach and apply this distributed learning method for the first time in the medical field to compare performance against (1) centrally hosted and (2) non collaborative configurations for a range of participants. Two medical deep learning tasks are used to compare split learning to conventional single and multi center approaches: a binary classification problem of a data set of 9000 fundus photos, and multi-label classification problem of a data set of 156,535 chest X-rays. The several distributed learning setups are compared for a range of 1-50 distributed participants. Performance of the split learning configuration remained constant for any number of clients compared to a single center study, showing a marked difference compared to the non collaborative configuration after 2 clients (p < 0.001) for both sets. Our results affirm the benefits of collaborative training of deep neural networks in health care. Our work proves the significant benefit of distributed learning in healthcare, and paves the way for future real-world implementations.

Related papers

• Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency [10.16245019262119]
  Federated learning aims to train a shared model of isolated clients without local data exchange. In this work, we propose a novel federated semi-supervised learning framework for medical image segmentation.
  arXiv  Detail & Related papers  (2024-03-19T12:52:38Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation [39.19170617818745]
  We address a prostate segmentation problem from MRI in a collaborative scenario. To the best of our knowledge, this is the first work in which consensus-based methods (CBM) are used to solve a problem of collaborative learning.
  arXiv  Detail & Related papers  (2023-09-29T09:47:18Z)
• Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance [65.61909544178603]
  We study a practical yet challenging problem of class imbalanced semi-supervised FL (imFed-Semi) This imFed-Semi problem is addressed by a novel dynamic bank learning scheme, which improves client training by exploiting class proportion information. We evaluate our approach on two public real-world medical datasets, including the intracranial hemorrhage diagnosis with 25,000 CT slices and skin lesion diagnosis with 10,015 dermoscopy images.
  arXiv  Detail & Related papers  (2022-06-27T06:51:48Z)
• 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)
• SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging [29.271291030933966]
  Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. In this study, we propose a novel heterogeneous-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning. We compare SplitAVG with seven state-of-the-art federated learning methods, using centrally hosted training data as the baseline.
  arXiv  Detail & Related papers  (2021-07-06T03:58:10Z)
• Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching [58.26619456972598]
  Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. This paper studies a practical yet challenging FL problem, named textitFederated Semi-supervised Learning (FSSL) We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme.
  arXiv  Detail & Related papers  (2021-06-16T07:58:00Z)
• Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation [12.940103904327655]
  We propose a novel dual-task mutual learning framework for semi-supervised medical image segmentation. Our framework can be formulated as an integration of two individual segmentation networks based on two tasks. By jointly learning the segmentation probability maps and signed distance maps of targets, our framework can enforce the geometric shape constraint and learn more reliable information.
  arXiv  Detail & Related papers  (2021-03-08T12:38:23Z)
• CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification [45.86448200141968]
  We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight studies of severe patients and direct them to a hospital or provide emergency medical care. We propose a multitask approach to consolidate both triage approaches and propose a convolutional neural network to combine all available labels within a single model. We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 32 COVID-19 studies, 30 cases with bacterial pneumonia, 31 healthy patients, and 30 patients with other lung pathologies to emulate a typical patient flow in
  arXiv  Detail & Related papers  (2020-06-02T08:05:06Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)

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.