Privacy-Preserving Constrained Domain Generalization via Gradient Alignment

• URL: http://arxiv.org/abs/2105.08511v3
• Date: Mon, 18 Sep 2023 08:57:44 GMT
• Title: Privacy-Preserving Constrained Domain Generalization via Gradient Alignment
• Authors: Chris Xing Tian, Haoliang Li, Yufei Wang, Shiqi Wang
• Abstract summary: Deep neural networks (DNN) have demonstrated unprecedented success for medical imaging applications. Due to the issue of limited dataset availability and the strict legal and ethical requirements for patient privacy protection, the broad applications of medical imaging classification have been largely hindered. In this paper, we aim to tackle this problem by developing the privacy-preserving constrained domain generalization method.
• Score: 37.916630896194334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNN) have demonstrated unprecedented success for medical imaging applications. However, due to the issue of limited dataset availability and the strict legal and ethical requirements for patient privacy protection, the broad applications of medical imaging classification driven by DNN with large-scale training data have been largely hindered. For example, when training the DNN from one domain (e.g., with data only from one hospital), the generalization capability to another domain (e.g., data from another hospital) could be largely lacking. In this paper, we aim to tackle this problem by developing the privacy-preserving constrained domain generalization method, aiming to improve the generalization capability under the privacy-preserving condition. In particular, We propose to improve the information aggregation process on the centralized server-side with a novel gradient alignment loss, expecting that the trained model can be better generalized to the "unseen" but related medical images. The rationale and effectiveness of our proposed method can be explained by connecting our proposed method with the Maximum Mean Discrepancy (MMD) which has been widely adopted as the distribution distance measurement. Experimental results on two challenging medical imaging classification tasks indicate that our method can achieve better cross-domain generalization capability compared to the state-of-the-art federated learning methods.

Related papers

• MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions [0.13108652488669734]
  integration of neural-network-based systems into clinical practice is limited by challenges related to domain generalization and robustness. We create and open-source MedMNIST-C, a benchmark dataset based on the MedMNIST+ collection covering 12 datasets and 9 imaging modalities.
  arXiv  Detail & Related papers  (2024-06-25T13:20:39Z)
• Generalizing Across Domains in Diabetic Retinopathy via Variational Autoencoders [0.0]
  Domain generalization for Diabetic Retinopathy classification allows a model to adeptly classify retinal images. In this study, we explore the inherent capacity of variational autoencoders to disentangle the latent space of fundus images.
  arXiv  Detail & Related papers  (2023-09-20T13:29:22Z)
• DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification [40.35834579068518]
  Domain shift between training and testing data presents a significant challenge for training general deep learning models. We introduce a DG method that re-establishes the model objective function as a pretrained model to the medical imaging field. Our proposed method consistently outperforms the previous state-of-the-art by a margin of 5.25% in average accuracy and a lower standard deviation.
  arXiv  Detail & Related papers  (2023-09-18T11:17:13Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [63.43592895652803]
  Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in federated learning can still suffer from performance drop when applied to completely unseen hospitals outside the federation. We present a novel approach, named as Episodic Learning in Continuous Frequency Space (ELCFS), for this problem. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts and in-depth ablation experiments on two medical image segmentation tasks.
  arXiv  Detail & Related papers  (2021-03-10T13:05:23Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging [4.396671464565882]
  We propose a new paradigm in which data from a diverse range of acquisition conditions are "harmonized" to a common reference domain. We test this approach on two example problems, namely MRI-based brain age prediction and classification of schizophrenia.
  arXiv  Detail & Related papers  (2020-10-11T22:01:37Z)
• Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [59.5104563755095]
  We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
  arXiv  Detail & Related papers  (2020-09-27T12:30:30Z)
• GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators [74.16405337436213]
  We propose Gradient-sanitized Wasserstein Generative Adrial Networks (GS-WGAN) GS-WGAN allows releasing a sanitized form of sensitive data with rigorous privacy guarantees. We find our approach consistently outperforms state-of-the-art approaches across multiple metrics.
  arXiv  Detail & Related papers  (2020-06-15T10:01:01Z)

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.