# ICFNet: Integrated Cross-modal Fusion Network for Survival Prediction

arXiv: https://arxiv.org/abs/2501.02778 · HTML: https://mlsanity.com/paper/2501.02778/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [ViLaCD-R1: A Vision-Language Framework for Semantic Change Detection in Remote Sensing](https://mlsanity.com/paper/2512.23244/) (2025)
- [Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Framework](https://mlsanity.com/paper/2509.20923/) (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)

## Abstract (arXiv)

Survival prediction is a crucial task in the medical field and is essential for optimizing treatment options and resource allocation. However, current methods often rely on limited data modalities, resulting in suboptimal performance. In this paper, we propose an Integrated Cross-modal Fusion Network (ICFNet) that integrates histopathology whole slide images, genomic expression profiles, patient demographics, and treatment protocols. Specifically, three types of encoders, a residual orthogonal decomposition module and a unification fusion module are employed to merge multi-modal features to enhance prediction accuracy. Additionally, a balanced negative log-likelihood loss function is designed to ensure fair training across different patients. Extensive experiments demonstrate that our ICFNet outperforms state-of-the-art algorithms on five public TCGA datasets, including BLCA, BRCA, GBMLGG, LUAD, and UCEC, and shows its potential to support clinical decision-making and advance precision medicine. The codes are available at: https://github.com/binging512/ICFNet.