# Memory-Augmented Incomplete Multimodal Survival Prediction via Cross-Slide and Gene-Attentive Hypergraph Learning

arXiv: https://arxiv.org/abs/2506.19324 · HTML: https://mlsanity.com/paper/2506.19324/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [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)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank](https://mlsanity.com/paper/2104.13415/) (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)
- [Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation](https://mlsanity.com/paper/1907.11150/) (2019)
- [Brain Tumor Segmentation on MRI with Missing Modalities](https://mlsanity.com/paper/1904.07290/) (2019)
- [Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction](https://mlsanity.com/paper/2401.01646/) (2024)
- [Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation](https://mlsanity.com/paper/2503.12068/) (2025)
- [Interpretable Image Classification via Non-parametric Part Prototype Learning](https://mlsanity.com/paper/2503.10247/) (2025)
- [Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance](https://mlsanity.com/paper/2505.11997/) (2025)
- [Approximate Equivariance SO(3) Needlet Convolution](https://mlsanity.com/paper/2206.10385/) (2022)
- [Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics](https://mlsanity.com/paper/2412.04072/) (2024)

## Built on by (most cited first, 4 of 4)

- [Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterization](https://mlsanity.com/paper/2508.16479/) (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)
- [PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities](https://mlsanity.com/paper/2604.04999/) (2026)

## Abstract (arXiv)

Multimodal pathology-genomic analysis is critical for cancer survival prediction. However, existing approaches predominantly integrate formalin-fixed paraffin-embedded (FFPE) slides with genomic data, while neglecting the availability of other preservation slides, such as Fresh Froze (FF) slides. Moreover, as the high-resolution spatial nature of pathology data tends to dominate the cross-modality fusion process, it hinders effective multimodal fusion and leads to modality imbalance challenges between pathology and genomics. These methods also typically require complete data modalities, limiting their clinical applicability with incomplete modalities, such as missing either pathology or genomic data. In this paper, we propose a multimodal survival prediction framework that leverages hypergraph learning to effectively integrate multi-WSI information and cross-modality interactions between pathology slides and genomics data while addressing modality imbalance. In addition, we introduce a memory mechanism that stores previously learned paired pathology-genomic features and dynamically compensates for incomplete modalities. Experiments on five TCGA datasets demonstrate that our model outperforms advanced methods by over 2.3% in C-Index. Under incomplete modality scenarios, our approach surpasses pathology-only (3.3%) and gene-only models (7.9%). Code: https://github.com/MCPathology/M2Surv