mlsanityData to 14 Oct 2026

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance

arXiv 2505.11997 ↗First posted 17 May 2025cs.CVcited by 11

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Abstract (arXiv)

Multimodal pathology-genomic analysis has become increasingly prominent in cancer survival prediction. However, existing studies mainly utilize multi-instance learning to aggregate patch-level features, neglecting the information loss of contextual and hierarchical details within pathology images. Furthermore, the disparity in data granularity and dimensionality between pathology and genomics leads to a significant modality imbalance. The high spatial resolution inherent in pathology data renders it a dominant role while overshadowing genomics in multimodal integration. In this paper, we propose a multimodal survival prediction framework that incorporates hypergraph learning to effectively capture both contextual and hierarchical details from pathology images. Moreover, it employs a modality rebalance mechanism and an interactive alignment fusion strategy to dynamically reweight the contributions of the two modalities, thereby mitigating the pathology-genomics imbalance. Quantitative and qualitative experiments are conducted on five TCGA datasets, demonstrating that our model outperforms advanced methods by over 3.4% in C-Index performance.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 25 of its 52 references that are in the map. First posted on arXiv 17 May 2025.

Builds on

25 of 52 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  3. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  4. 2017Self-Normalizing Neural Networkscited by 598
  5. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  6. 2018Hypergraph Neural Networkscited by 488
  7. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  8. 2019Addressing Failure Prediction by Learning Model Confidencecited by 186
  9. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  10. 2019Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosiscited by 165
  11. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  12. 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
13 more
  1. 2023Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Predictioncited by 92
  2. 2023Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Predictioncited by 86
  3. 2023Cross-Modal Translation and Alignment for Survival Analysiscited by 70
  4. 2023Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learningcited by 47
  5. 2024Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Predictioncited by 41
  6. 2020Representation Learning of Histopathology Images using Graph Neural Networkscited by 40
  7. 2023Sheaf Hypergraph Networkscited by 34
  8. 2023SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathologycited by 25
  9. 2023AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Imagescited by 18
  10. 2024Predictive Dynamic Fusioncited by 17
  11. 2025Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentationcited by 17
  12. 2024HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classificationcited by 13
  13. 2024Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomicscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 4.

11 papers.

  1. 2025Spatially Gene Expression Prediction using Dual-Scale Contrastive Learningcited by 7
  2. 2025Memory-Augmented Incomplete Multimodal Survival Prediction via Cross-Slide and Gene-Attentive Hypergraph Learningcited by 4
  3. 2025Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterizationcited by 2
  4. 2025Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysiscited by 1
  5. 2025GAOT: Generating Articulated Objects Through Text-Guided Diffusion Modelscited by 1
  6. 2025Cross-Stain Contrastive Learning for Paired Immunohistochemistry and Histopathology Slide Representation Learningcited by 0
  7. 2025Harnessing Hypergraphs in Geometric Deep Learning for 3D RNA Inverse Foldingcited by 0
  8. 2026DCG ReID: Disentangling Collaboration and Guidance Fusion Representations for Multi-modal Vehicle Re-Identificationcited by 0
  9. 2026Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Predictioncited by 0
  10. 2026ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysiscited by 0
  11. 2026An Accurate and Interpretable Hyper Graph Neural Network for GBM Survival Predictioncited by 0