Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation

• URL: http://arxiv.org/abs/2004.00074v1
• Date: Tue, 31 Mar 2020 19:43:59 GMT
• Title: Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation
• Authors: Karthik Gopinath, Christian Desrosiers, and Herve Lombaert
• Abstract summary: Recent developments have enabled learning surface data directly across multiple brain surfaces via graph convolutions on cortical data. Adversarial training is widely used for domain adaptation to improve the segmentation performance across domains. We demonstrate an 8% mean improvement over a non-adversarial training strategy applied on multiple target domains extracted from MindBoggle.
• Score: 9.430867304159179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The varying cortical geometry of the brain creates numerous challenges for its analysis. Recent developments have enabled learning surface data directly across multiple brain surfaces via graph convolutions on cortical data. However, current graph learning algorithms do fail when brain surface data are misaligned across subjects, thereby affecting their ability to deal with data from multiple domains. Adversarial training is widely used for domain adaptation to improve the segmentation performance across domains. In this paper, adversarial training is exploited to learn surface data across inconsistent graph alignments. This novel approach comprises a segmentator that uses a set of graph convolution layers to enable parcellation directly across brain surfaces in a source domain, and a discriminator that predicts a graph domain from segmentations. More precisely, the proposed adversarial network learns to generalize a parcellation across both, source and target domains. We demonstrate an 8% mean improvement in performance over a non-adversarial training strategy applied on multiple target domains extracted from MindBoggle, the largest publicly available manually-labeled brain surface dataset.

Related papers

• GDAIP: A Graph-Based Domain Adaptive Framework for Individual Brain Parcellation [14.297591748463907]
  Graph Domain Adaptation for Individual Parcellation (GDAIP) is a novel framework that integrates Graph Attention Networks (GAT) with Minimax Entropy (MME)-based domain adaptation.<n>We construct cross-dataset brain graphs at both the group and individual levels.
  arXiv  Detail & Related papers  (2025-07-29T12:04:09Z)
• SA-GDA: Spectral Augmentation for Graph Domain Adaptation [38.71041292000361]
  Graph neural networks (GNNs) have achieved impressive impressions for graph-related tasks. This paper presents the textitSpectral Augmentation for Graph Domain Adaptation (method) for graph node classification. We develop a dual graph convolutional network to jointly exploits local and global consistency for feature aggregation.
  arXiv  Detail & Related papers  (2024-08-17T13:01:45Z)
• Domain Adaptive Graph Classification [0.0]
  We introduce the Dual Adversarial Graph Representation Learning (DAGRL), which explore the graph topology from dual branches and mitigate domain discrepancies via dual adversarial learning. Our approach incorporates adaptive perturbations into the dual branches, which align the source and target distribution to address domain discrepancies.
  arXiv  Detail & Related papers  (2023-12-21T02:37:56Z)
• Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching [14.876681993079062]
  We propose a graph-based framework to explore the local-level feature alignment between the two domains. We also formulate a category-guided contrastive loss to guide the segmentation model to learn discriminative features on the target domain.
  arXiv  Detail & Related papers  (2022-08-09T02:30:15Z)
• Graph Domain Adaptation: A Generative View [24.92864775423047]
  We propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data. Our method significantly outperforms the traditional domain adaptation methods and the disentangled-based domain adaptation methods.
  arXiv  Detail & Related papers  (2021-06-14T15:11:57Z)
• Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions [9.581605678437032]
  This work addresses the problem of unsupervised domain adaptation for LiDAR semantic segmentation models. Our approach combines novel ideas on top of the current state-of-the-art approaches and yields new state-of-the-art results.
  arXiv  Detail & Related papers  (2020-10-23T08:52:15Z)
• A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [81.07994783143533]
  Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
  arXiv  Detail & Related papers  (2020-09-01T00:06:50Z)
• Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning [85.6386289476598]
  We develop a novel adversarial graph representation adaptation (AGRA) framework for cross-domain holistic-local feature co-adaptation. We conduct extensive and fair evaluations on several popular benchmarks and show that the proposed AGRA framework outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-03T15:00:31Z)
• Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition [86.25926461936412]
  We propose a novel Adrialversa Graph Representation Adaptation (AGRA) framework that unifies graph representation propagation with adversarial learning for cross-domain holistic-local feature co-adaptation. We conduct extensive and fair experiments on several popular benchmarks and show that the proposed AGRA framework achieves superior performance over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-03T13:27:24Z)
• Adversarial Bipartite Graph Learning for Video Domain Adaptation [50.68420708387015]
  Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area. Recent works on visual domain adaptation which leverage adversarial learning to unify the source and target video representations are not highly effective on the videos. This paper proposes an Adversarial Bipartite Graph (ABG) learning framework which directly models the source-target interactions.
  arXiv  Detail & Related papers  (2020-07-31T03:48:41Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)
• Supervised Domain Adaptation using Graph Embedding [86.3361797111839]
  Domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. We propose a generic framework based on graph embedding. We show that the proposed approach leads to a powerful Domain Adaptation framework.
  arXiv  Detail & Related papers  (2020-03-09T12:25:13Z)

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.