mlsanityData to 7 Oct 2026

Multimodal Information Fusion for Glaucoma and DR Classification

arXiv 2209.00979 ↗First posted 2 Sep 2022eess.IV cs.CV cs.LGcited by 11

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Abstract (arXiv)

Multimodal information is frequently available in medical tasks. By combining information from multiple sources, clinicians are able to make more accurate judgments. In recent years, multiple imaging techniques have been used in clinical practice for retinal analysis: 2D fundus photographs, 3D optical coherence tomography (OCT) and 3D OCT angiography, etc. Our paper investigates three multimodal information fusion strategies based on deep learning to solve retinal analysis tasks: early fusion, intermediate fusion, and hierarchical fusion. The commonly used early and intermediate fusions are simple but do not fully exploit the complementary information between modalities. We developed a hierarchical fusion approach that focuses on combining features across multiple dimensions of the network, as well as exploring the correlation between modalities. These approaches were applied to glaucoma and diabetic retinopathy classification, using the public GAMMA dataset (fundus photographs and OCT) and a private dataset of PlexElite 9000 (Carl Zeis Meditec Inc.) OCT angiography acquisitions, respectively. Our hierarchical fusion method performed the best in both cases and paved the way for better clinical diagnosis.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 2 Sep 2022.

Builds on

5 of 22 references.

References · 5

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020A review: Deep learning for medical image segmentation using multi-modality fusioncited by 84
  3. 2018HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentationcited by 66
  4. 2018Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challengecited by 56
  5. 2022GAMMA Challenge:Glaucoma grAding from Multi-Modality imAgescited by 45

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Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 6; 2025: 1.

11 papers.

  1. 2024A review of deep learning-based information fusion techniques for multimodal medical image classificationcited by 25
  2. 2024A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applicationscited by 25
  3. 2024MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Imagescited by 9
  4. 2023Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Imagescited by 7
  5. 2023Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Imagescited by 6
  6. 2024Confidence-aware multi-modality learning for eye disease screeningcited by 5
  7. 2022Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathycited by 4
  8. 2024DISCOVER: 2-D Multiview Summarization of Optical Coherence Tomography Angiography for Automatic Diabetic Retinopathy Diagnosiscited by 3
  9. 2025A Survey of Multimodal Ophthalmic Diagnostics: From Task-Specific Approaches to Foundational Modelscited by 3
  10. 2024Masked Image Modelling for retinal OCT understandingcited by 2
  11. 2023Reliable Multimodality Eye Disease Screening via Mixture of Student's t Distributionscited by 1