mlsanityData to 14 Oct 2026

Heterogeneous Graph Learning for Multi-modal Medical Data Analysis

arXiv 2211.15158 ↗First posted 28 Nov 2022cs.CV cs.AI cs.LGcited by 16

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Abstract (arXiv)

Routine clinical visits of a patient produce not only image data, but also non-image data containing clinical information regarding the patient, i.e., medical data is multi-modal in nature. Such heterogeneous modalities offer different and complementary perspectives on the same patient, resulting in more accurate clinical decisions when they are properly combined. However, despite its significance, how to effectively fuse the multi-modal medical data into a unified framework has received relatively little attention. In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data. Specifically, we construct a multiplex network that incorporates multiple types of non-image features of patients to capture the complex relationship between patients in a systematic way, which leads to more accurate clinical decisions. Extensive experiments on various real-world datasets demonstrate the superiority and practicality of HetMed. The source code for HetMed is available at https://github.com/Sein-Kim/Multimodal-Medical.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 27 of its 65 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

27 of 65 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
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15 more
  1. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  2. 20203D Self-Supervised Methods for Medical Imagingcited by 134
  3. 2015A Survey of Heterogeneous Information Network Analysiscited by 110
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  8. 20203D Deep Learning on Medical Images: A Reviewcited by 76
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  15. 2020Context Matters: Graph-based Self-supervised Representation Learning for Medical Imagescited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 4; 2026: 3.

16 papers.

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  1. 2024Learning on Multimodal Graphs: A Surveycited by 22
  2. 2024Multimodal Data Integration for Precision Oncology: Challenges and Future Directionscited by 10
  3. 2023Task-Equivariant Graph Few-shot Learningcited by 8
  4. 2024AliFuse: Aligning and Fusing Multi-modal Medical Data for Computer-Aided Diagnosiscited by 7
  5. 2024GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representationcited by 7
  6. 2025HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer's Diseasecited by 3
  7. 2024FOAA: Flattened Outer Arithmetic Attention For Multimodal Tumor Classificationcited by 2
  8. 2023Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classificationcited by 1
  9. 2024Uncertainty-Aware Hardware Trojan Detection Using Multimodal Deep Learningcited by 1
  10. 2025Representation Learning with Mutual Influence of Modalities for Node Classification in Multi-Modal Heterogeneous Networkscited by 1
  11. 2026LeMoF: Level-guided Multimodal Fusion for Heterogeneous Clinical Datacited by 1
  12. 2026Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learningcited by 1
4 more
  1. 2024GAMMA-PD: Graph-based Analysis of Multi-Modal Motor Impairment Assessments in Parkinson's Diseasecited by 0
  2. 2025Multimodal Sheaf-based Network for Glioblastoma Molecular Subtype Predictioncited by 0
  3. 2025Bridging Graph and State-Space Modeling for Intensive Care Unit Length of Stay Predictioncited by 0
  4. 2026MRI-based Deep Radiomic Phenotyping of Neuromuscular Disorders: A Topology-driven Characterizationcited by 0