# PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images

arXiv: https://arxiv.org/abs/2411.00749 · HTML: https://mlsanity.com/paper/2411.00749/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Cross-Modal Translation and Alignment for Survival Analysis](https://mlsanity.com/paper/2309.12855/) (2023)
- [Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction](https://mlsanity.com/paper/2307.11952/) (2023)
- [Advancing Gene Selection in Oncology: A Fusion of Deep Learning and Sparsity for Precision Gene Selection](https://mlsanity.com/paper/2403.01927/) (2024)

## Built on by (most cited first, 2 of 2)

- [Survival Modeling from Whole Slide Images via Patch-Level Graph Clustering and Mixture Density Experts](https://mlsanity.com/paper/2507.16476/) (2025)
- [Spatially-Aware Mixture of Experts with Log-Logistic Survival Modeling for Whole-Slide Images](https://mlsanity.com/paper/2511.06266/) (2025)

## Abstract (arXiv)

Accurate survival prediction is essential for personalized cancer treatment. However, genomic data - often a more powerful predictor than pathology data - is costly and inaccessible. We present the cross-modal genomic feature translation and alignment network for enhanced survival prediction from histopathology images (PathoGen-X). It is a deep learning framework that leverages both genomic and imaging data during training, relying solely on imaging data at testing. PathoGen-X employs transformer-based networks to align and translate image features into the genomic feature space, enhancing weaker imaging signals with stronger genomic signals. Unlike other methods, PathoGen-X translates and aligns features without projecting them to a shared latent space and requires fewer paired samples. Evaluated on TCGA-BRCA, TCGA-LUAD, and TCGA-GBM datasets, PathoGen-X demonstrates strong survival prediction performance, emphasizing the potential of enriched imaging models for accessible cancer prognosis.