Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification

• URL: http://arxiv.org/abs/2204.13591v1
• Date: Tue, 26 Apr 2022 20:17:36 GMT
• Title: Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification
• Authors: Yixing Huang, Christoph Bert, Stefan Fischer, Manuel Schmidt, Arnd D\"orfler, Andreas Maier, Rainer Fietkau, Florian Putz
• Abstract summary: Continual learning, as one approach to peer-to-peer federated learning, can promote multicenter collaboration on deep learning algorithm development. Our experiments demonstrate that continual learning can improve brain metastasis identification performance for centers with limited data.
• Score: 8.071094228545297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to data privacy constraints, data sharing among multiple centers is restricted. Continual learning, as one approach to peer-to-peer federated learning, can promote multicenter collaboration on deep learning algorithm development by sharing intermediate models instead of training data. This work aims to investigate the feasibility of continual learning for multicenter collaboration on an exemplary application of brain metastasis identification using DeepMedic. 920 T1 MRI contrast enhanced volumes are split to simulate multicenter collaboration scenarios. A continual learning algorithm, synaptic intelligence (SI), is applied to preserve important model weights for training one center after another. In a bilateral collaboration scenario, continual learning with SI achieves a sensitivity of 0.917, and naive continual learning without SI achieves a sensitivity of 0.906, while two models trained on internal data solely without continual learning achieve sensitivity of 0.853 and 0.831 only. In a seven-center multilateral collaboration scenario, the models trained on internal datasets (100 volumes each center) without continual learning obtain a mean sensitivity value of 0.725. With single-visit continual learning (i.e., the shared model visits each center only once during training), the sensitivity is improved to 0.788 and 0.849 without SI and with SI, respectively. With iterative continual learning (i.e., the shared model revisits each center multiple times during training), the sensitivity is further improved to 0.914, which is identical to the sensitivity using mixed data for training. Our experiments demonstrate that continual learning can improve brain metastasis identification performance for centers with limited data. This study demonstrates the feasibility of applying continual learning for peer-to-peer federated learning in multicenter collaboration.

Related papers

• Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation [2.479757014250359]
  This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance. incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data.
  arXiv  Detail & Related papers  (2024-05-17T16:01:11Z)
• Federated learning model for predicting major postoperative complications [2.565552377354702]
  We developed federated learning models to predict nine major postoperative complications. We compared federated learning models with local learning models trained on a single site and central learning models trained on pooled dataset from two centers. Our federated learning model obtained comparable performance to the best local learning model at each center, demonstrating strong generalizability.
  arXiv  Detail & Related papers  (2024-04-09T22:31:10Z)
• Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning [57.652899266553035]
  Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server. We propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs.
  arXiv  Detail & Related papers  (2024-03-11T09:21:11Z)
• A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration [6.064986446665161]
  Data privacy constraints impede the development of high performance deep learning models from multicenter collaboration. Weight transfer methods share intermediate model weights without raw data and hence can bypass data privacy restrictions. Performance drops are observed when the model is transferred from one center to the next because of forgetting the problem. In this work, a conventional domain/task incremental learning framework is adapted for incremental transfer learning.
  arXiv  Detail & Related papers  (2023-09-29T12:43:21Z)
• 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)
• Scalable Collaborative Learning via Representation Sharing [53.047460465980144]
  Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device) In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss.
  arXiv  Detail & Related papers  (2022-11-20T10:49:22Z)
• Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis [48.64462717254158]
  We developed a self-supervised contrastive learning approach, EchoCLR, to catered to echocardiogram videos. When fine-tuned on small portions of labeled data, EchoCLR pretraining significantly improved classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) EchoCLR is unique in its ability to learn representations of medical videos and demonstrates that SSL can enable label-efficient disease classification from small, labeled datasets.
  arXiv  Detail & Related papers  (2022-07-23T19:17:26Z)
• 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)
• 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)
• A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning [49.15799302636519]
  We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers) In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns. We show that it is possible to achieve equivalent learning performance as if the data is available in a single place.
  arXiv  Detail & Related papers  (2020-09-29T13:08:12Z)
• Automatic sleep stage classification with deep residual networks in a mixed-cohort setting [63.52264764099532]
  We developed a novel deep neural network model to assess the generalizability of several large-scale cohorts. Overall classification accuracy improved with increasing fractions of training data.
  arXiv  Detail & Related papers  (2020-08-21T10:48:35Z)

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.