Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation

• URL: http://arxiv.org/abs/2309.17097v2
• Date: Mon, 2 Oct 2023 07:45:47 GMT
• Title: Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation
• Authors: Lucia Innocenti, Michela Antonelli, Francesco Cremonesi, Kenaan Sarhan, Alejandro Granados, Vicky Goh, Sebastien Ourselin, Marco Lorenzi
• Abstract summary: 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.
• Score: 39.19170617818745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use them for the development of machine learning and deep learning models, which are known to be data-hungry. One way to overcome this limitation is to use collaborative learning (CL) methods, which allow hospitals to work collaboratively to solve a task, without the need to explicitly share local data. In this paper, we address a prostate segmentation problem from MRI in a collaborative scenario by comparing two different approaches: federated learning (FL) and consensus-based methods (CBM). To the best of our knowledge, this is the first work in which CBM, such as label fusion techniques, are used to solve a problem of collaborative learning. In this setting, CBM combine predictions from locally trained models to obtain a federated strong learner with ideally improved robustness and predictive variance properties. Our experiments show that, in the considered practical scenario, CBMs provide equal or better results than FL, while being highly cost-effective. Our results demonstrate that the consensus paradigm may represent a valid alternative to FL for typical training tasks in medical imaging.

Related papers

• Medical Federated Model with Mixture of Personalized and Sharing Components [31.068735334318088]
  We propose a new personalized framework of federated learning to handle the problem. It successfully yields personalized models based on awareness of similarity between local data. Also, we propose an effective method to reduce the computational cost, which improves computation efficiency significantly.
  arXiv  Detail & Related papers  (2023-06-26T07:50:32Z)
• 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)
• Federated Cross Learning for Medical Image Segmentation [23.075410916203005]
  Federated learning (FL) can collaboratively train deep learning models using isolated patient data owned by different hospitals for various clinical applications. A major problem of FL is its performance degradation when dealing with data that are not independently and identically distributed (non-iid)
  arXiv  Detail & Related papers  (2022-04-05T18:55:02Z)
• Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation [66.44449514373746]
  Cross-silo federated learning (FL) has attracted much attention in medical imaging analysis with deep learning in recent years. There can be a gap between the model trained from FL and one from centralized training. We propose a novel training framework FedSM to avoid client issue and successfully close the drift gap.
  arXiv  Detail & Related papers  (2022-03-18T19:50:07Z)
• Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases [57.90226879210227]
  FedCy is a semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases.
  arXiv  Detail & Related papers  (2022-03-14T17:44:53Z)
• 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)
• 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)
• Split Learning for collaborative deep learning in healthcare [12.128894212674917]
  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.
  arXiv  Detail & Related papers  (2019-12-27T14:39:58Z)

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.