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

arXiv: https://arxiv.org/abs/2505.11997 · HTML: https://mlsanity.com/paper/2505.11997/

## 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 (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Addressing Failure Prediction by Learning Model Confidence](https://mlsanity.com/paper/1910.04851/) (2019)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis](https://mlsanity.com/paper/1912.08937/) (2019)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks](https://mlsanity.com/paper/2107.13048/) (2021)
- [Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction](https://mlsanity.com/paper/2304.06819/) (2023)
- [Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction](https://mlsanity.com/paper/2306.08330/) (2023)
- [Cross-Modal Translation and Alignment for Survival Analysis](https://mlsanity.com/paper/2309.12855/) (2023)
- [Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2307.04189/) (2023)
- [Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction](https://mlsanity.com/paper/2401.01646/) (2024)
- [Representation Learning of Histopathology Images using Graph Neural Networks](https://mlsanity.com/paper/2004.07399/) (2020)
- [Sheaf Hypergraph Networks](https://mlsanity.com/paper/2309.17116/) (2023)
- [SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology](https://mlsanity.com/paper/2312.15010/) (2023)
- [AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images](https://mlsanity.com/paper/2303.00865/) (2023)
- [Predictive Dynamic Fusion](https://mlsanity.com/paper/2406.04802/) (2024)
- [Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation](https://mlsanity.com/paper/2503.12068/) (2025)
- [HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification](https://mlsanity.com/paper/2411.07660/) (2024)
- [Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics](https://mlsanity.com/paper/2412.04072/) (2024)

## Built on by (most cited first, 11 of 11)

- [Spatially Gene Expression Prediction using Dual-Scale Contrastive Learning](https://mlsanity.com/paper/2506.23827/) (2025)
- [Memory-Augmented Incomplete Multimodal Survival Prediction via Cross-Slide and Gene-Attentive Hypergraph Learning](https://mlsanity.com/paper/2506.19324/) (2025)
- [Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterization](https://mlsanity.com/paper/2508.16479/) (2025)
- [Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis](https://mlsanity.com/paper/2511.18089/) (2025)
- [GAOT: Generating Articulated Objects Through Text-Guided Diffusion Models](https://mlsanity.com/paper/2512.03566/) (2025)
- [Cross-Stain Contrastive Learning for Paired Immunohistochemistry and Histopathology Slide Representation Learning](https://mlsanity.com/paper/2512.03577/) (2025)
- [Harnessing Hypergraphs in Geometric Deep Learning for 3D RNA Inverse Folding](https://mlsanity.com/paper/2512.03592/) (2025)
- [DCG ReID: Disentangling Collaboration and Guidance Fusion Representations for Multi-modal Vehicle Re-Identification](https://mlsanity.com/paper/2601.02924/) (2026)
- [Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction](https://mlsanity.com/paper/2603.02162/) (2026)
- [ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis](https://mlsanity.com/paper/2606.19140/) (2026)
- [An Accurate and Interpretable Hyper Graph Neural Network for GBM Survival Prediction](https://mlsanity.com/paper/2609.25088/) (2026)

## 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.