# Robust Multimodal Survival Prediction with the Latent Differentiation Conditional Variational AutoEncoder

arXiv: https://arxiv.org/abs/2503.09496 · HTML: https://mlsanity.com/paper/2503.09496/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [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)
- [Nystr"omformer: A Nystr"om-Based Algorithm for Approximating Self-Attention](https://mlsanity.com/paper/2102.03902/) (2021)
- [SMIL: Multimodal Learning with Severely Missing Modality](https://mlsanity.com/paper/2103.05677/) (2021)
- [Multimodal Generative Models for Scalable Weakly-Supervised Learning](https://mlsanity.com/paper/1802.05335/) (2018)
- [Are Multimodal Transformers Robust to Missing Modality?](https://mlsanity.com/paper/2204.05454/) (2022)
- [Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis](https://mlsanity.com/paper/1912.08937/) (2019)
- [Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling](https://mlsanity.com/paper/2307.14126/) (2023)
- [Multimodal Prompting with Missing Modalities for Visual Recognition](https://mlsanity.com/paper/2303.03369/) (2023)
- [HeMIS: Hetero-Modal Image Segmentation](https://mlsanity.com/paper/1607.05194/) (2016)
- [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)
- [Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction](https://mlsanity.com/paper/2401.01646/) (2024)
- [Regression-based Deep-Learning predicts molecular biomarkers from pathology slides](https://mlsanity.com/paper/2304.05153/) (2023)

## Built on by (most cited first, 13 of 13)

- [Multimodal Model for Computational Pathology:Representation Learning and Image Compression](https://mlsanity.com/paper/2603.18660/) (2026)
- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterization](https://mlsanity.com/paper/2508.16479/) (2025)
- [Sparse Task Vector Mixup with Hypernetworks for Efficient Knowledge Transfer in Whole-Slide Image Prognosis](https://mlsanity.com/paper/2603.10526/) (2026)
- [Cross-Cancer Knowledge Transfer in WSI-based Prognosis Prediction](https://mlsanity.com/paper/2508.13482/) (2025)
- [A Flow Model with Low-Rank Transformers for Incomplete Multimodal Survival Analysis](https://mlsanity.com/paper/2510.21829/) (2025)
- [MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality](https://mlsanity.com/paper/2603.26071/) (2026)
- [Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction](https://mlsanity.com/paper/2510.06113/) (2025)
- [Structural Prognostic Event Modeling for Multimodal Cancer Survival Analysis](https://mlsanity.com/paper/2512.01116/) (2025)
- [WSD-MIL: Window Scale Decay Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2512.19982/) (2025)
- [Advancing Cancer Prognosis with Hierarchical Fusion of Genomic, Proteomic and Pathology Imaging Data from a Systems Biology Perspective](https://mlsanity.com/paper/2603.13787/) (2026)
- [PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities](https://mlsanity.com/paper/2604.04999/) (2026)
- [AdaSurvMamba: Dynamic Fusion and Semantic Scanning for Multimodal Survival Analysis](https://mlsanity.com/paper/2607.16260/) (2026)

## Abstract (arXiv)

The integrative analysis of histopathological images and genomic data has received increasing attention for survival prediction of human cancers. However, the existing studies always hold the assumption that full modalities are available. As a matter of fact, the cost for collecting genomic data is high, which sometimes makes genomic data unavailable in testing samples. A common way of tackling such incompleteness is to generate the genomic representations from the pathology images. Nevertheless, such strategy still faces the following two challenges: (1) The gigapixel whole slide images (WSIs) are huge and thus hard for representation. (2) It is difficult to generate the genomic embeddings with diverse function categories in a unified generative framework. To address the above challenges, we propose a Conditional Latent Differentiation Variational AutoEncoder (LD-CVAE) for robust multimodal survival prediction, even with missing genomic data. Specifically, a Variational Information Bottleneck Transformer (VIB-Trans) module is proposed to learn compressed pathological representations from the gigapixel WSIs. To generate different functional genomic features, we develop a novel Latent Differentiation Variational AutoEncoder (LD-VAE) to learn the common and specific posteriors for the genomic embeddings with diverse functions. Finally, we use the product-of-experts technique to integrate the genomic common posterior and image posterior for the joint latent distribution estimation in LD-CVAE. We test the effectiveness of our method on five different cancer datasets, and the experimental results demonstrate its superiority in both complete and missing modality scenarios.