GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference

• URL: http://arxiv.org/abs/2104.03597v1
• Date: Thu, 8 Apr 2021 08:23:37 GMT
• Title: GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
• Authors: Mahsa Ghorbani, Mojtaba Bahrami, Anees Kazi, Mahdieh SoleymaniBaghshah, Hamid R. Rabiee, and Nassir Navab
• Abstract summary: We propose a novel semi-supervised approach named GKD based on knowledge distillation. We perform experiments on two public datasets for diagnosing Autism spectrum disorder, and Alzheimer's disease. According to these experiments, GKD outperforms the previous graph-based deep learning methods in terms of accuracy, AUC, and Macro F1.
• Score: 41.348451615460796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increased amount of multi-modal medical data has opened the opportunities to simultaneously process various modalities such as imaging and non-imaging data to gain a comprehensive insight into the disease prediction domain. Recent studies using Graph Convolutional Networks (GCNs) provide novel semi-supervised approaches for integrating heterogeneous modalities while investigating the patients' associations for disease prediction. However, when the meta-data used for graph construction is not available at inference time (e.g., coming from a distinct population), the conventional methods exhibit poor performance. To address this issue, we propose a novel semi-supervised approach named GKD based on knowledge distillation. We train a teacher component that employs the label-propagation algorithm besides a deep neural network to benefit from the graph and non-graph modalities only in the training phase. The teacher component embeds all the available information into the soft pseudo-labels. The soft pseudo-labels are then used to train a deep student network for disease prediction of unseen test data for which the graph modality is unavailable. We perform our experiments on two public datasets for diagnosing Autism spectrum disorder, and Alzheimer's disease, along with a thorough analysis on synthetic multi-modal datasets. According to these experiments, GKD outperforms the previous graph-based deep learning methods in terms of accuracy, AUC, and Macro F1.

Related papers

• Classification of developmental and brain disorders via graph convolutional aggregation [6.6356049194991815]
  We introduce an aggregator normalization graph convolutional network by leveraging aggregation in graph sampling. The proposed model learns discriminative graph node representations by incorporating both imaging and non-imaging features into the graph nodes and edges. We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer's Disease Neuroimaging Initiative (ADNI)
  arXiv  Detail & Related papers  (2023-11-13T14:36:29Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification [56.17097897754628]
  We show that transductive linear probing with self-supervised graph contrastive pretraining can outperform the state-of-the-art fully supervised meta-learning based methods under the same protocol. We hope this work can shed new light on few-shot node classification problems and foster future research on learning from scarcely labeled instances on graphs.
  arXiv  Detail & Related papers  (2022-12-11T21:10:34Z)
• Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications [52.65389473899139]
  Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Recent works have shown that considering relationships between input data samples have a positive regularizing effect for the downstream task. We propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications.
  arXiv  Detail & Related papers  (2022-04-01T10:01:37Z)
• Multi-modal Graph Learning for Disease Prediction [35.156975779372836]
  We propose an end-to-end Multi-modal Graph Learning framework (MMGL) for disease prediction with multi-modality. Instead of defining the graph manually, the latent graph structure is captured through an effective way of adaptive graph learning. An extensive group of experiments on two disease prediction tasks demonstrates that the proposed MMGL achieves more favorable performance.
  arXiv  Detail & Related papers  (2022-03-11T12:33:20Z)
• Multi-modal Graph Learning for Disease Prediction [35.4310911850558]
  We propose an end-to-end Multimodal Graph Learning framework (MMGL) for disease prediction. Instead of defining the adjacency matrix manually as existing methods, the latent graph structure can be captured through a novel way of adaptive graph learning.
  arXiv  Detail & Related papers  (2021-07-01T03:59:22Z)
• Weakly-supervised Graph Meta-learning for Few-shot Node Classification [53.36828125138149]
  We propose a new graph meta-learning framework -- Graph Hallucination Networks (Meta-GHN) Based on a new robustness-enhanced episodic training, Meta-GHN is meta-learned to hallucinate clean node representations from weakly-labeled data. Extensive experiments demonstrate the superiority of Meta-GHN over existing graph meta-learning studies.
  arXiv  Detail & Related papers  (2021-06-12T22:22:10Z)
• Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction [7.6146285961466]
  We propose a generalizable framework that can automatically integrate imaging data with non-imaging data in populations for uncertainty-aware disease prediction. Experimental results on four databases show that our method can consistently and significantly improve the diagnostic accuracy for Autism spectrum disorder, Alzheimer's disease, and ocular diseases.
  arXiv  Detail & Related papers  (2020-09-06T15:53:17Z)
• Latent-Graph Learning for Disease Prediction [44.26665239213658]
  We show that it is possible to learn a single, optimal graph towards the GCN's downstream task of disease classification. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well.
  arXiv  Detail & Related papers  (2020-03-27T08:18:01Z)
• Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels [66.57101219176275]
  Disease diagnosis on chest X-ray images is a challenging multi-label classification task. We propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases. Our method is the first to build a graph over the feature maps with a dynamic adjacency matrix for correlation learning.
  arXiv  Detail & Related papers  (2020-02-26T17:10:48Z)

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.