MurreNet: Modeling Holistic Multimodal Interactions Between Histopathology and Genomic Profiles for Survival Prediction

• URL: http://arxiv.org/abs/2507.04891v1
• Date: Mon, 07 Jul 2025 11:26:29 GMT
• Title: MurreNet: Modeling Holistic Multimodal Interactions Between Histopathology and Genomic Profiles for Survival Prediction
• Authors: Mingxin Liu, Chengfei Cai, Jun Li, Pengbo Xu, Jinze Li, Jiquan Ma, Jun Xu,
• Abstract summary: This paper presents a Multimodal Representation Decoupling Network (MurreNet) to advance cancer survival analysis.<n>MurreNet decomposes paired input data into modality-specific and modality-shared representations, thereby reducing redundancy between modalities.<n>Experiments conducted on six TCGA cancer cohorts demonstrate that our MurreNet achieves state-of-the-art (SOTA) performance in survival prediction.
• Score: 5.895727565919295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cancer survival prediction requires integrating pathological Whole Slide Images (WSIs) and genomic profiles, a challenging task due to the inherent heterogeneity and the complexity of modeling both inter- and intra-modality interactions. Current methods often employ straightforward fusion strategies for multimodal feature integration, failing to comprehensively capture modality-specific and modality-common interactions, resulting in a limited understanding of multimodal correlations and suboptimal predictive performance. To mitigate these limitations, this paper presents a Multimodal Representation Decoupling Network (MurreNet) to advance cancer survival analysis. Specifically, we first propose a Multimodal Representation Decomposition (MRD) module to explicitly decompose paired input data into modality-specific and modality-shared representations, thereby reducing redundancy between modalities. Furthermore, the disentangled representations are further refined then updated through a novel training regularization strategy that imposes constraints on distributional similarity, difference, and representativeness of modality features. Finally, the augmented multimodal features are integrated into a joint representation via proposed Deep Holistic Orthogonal Fusion (DHOF) strategy. Extensive experiments conducted on six TCGA cancer cohorts demonstrate that our MurreNet achieves state-of-the-art (SOTA) performance in survival prediction.

Related papers

• AdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival Prediction [29.11927806428086]
  AdaMHF is a framework designed for efficient, comprehensive, and tailored feature extraction and fusion.<n>It is specifically adapted to the uniqueness of medical data, enabling accurate predictions with minimal resource consumption.<n>Extensive experiments on TCGA datasets demonstrate that AdaMHF surpasses current state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-27T03:27:55Z)
• Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction [0.0]
  We propose Disentangled and Interpretable Multimodal Attention Fusion (DIMAF) to improve cancer survival prediction.<n>DIMAF separates intra- and inter-modal interactions within an attention-based fusion mechanism to learn distinct modality-specific and modality-shared representations.<n>We evaluate DIMAF on four public cancer survival datasets, achieving a relative average improvement of 1.85% in performance and 23.7% in disentanglement.
  arXiv  Detail & Related papers  (2025-03-20T12:02:10Z)
• Continually Evolved Multimodal Foundation Models for Cancer Prognosis [50.43145292874533]
  Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates.<n>Previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information.<n>Existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals.<n>Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities.
  arXiv  Detail & Related papers  (2025-01-30T06:49:57Z)
• M2EF-NNs: Multimodal Multi-instance Evidence Fusion Neural Networks for Cancer Survival Prediction [24.323961146023358]
  We propose a neural network model called M2EF-NNs for accurate cancer survival prediction. To capture global information in the images, we use a pre-trained Vision Transformer (ViT) model. We are the first to apply the Dempster-Shafer evidence theory (DST) to cancer survival prediction.
  arXiv  Detail & Related papers  (2024-08-08T02:31:04Z)
• SurvMamba: State Space Model with Multi-grained Multi-modal Interaction for Survival Prediction [8.452410804749512]
  We propose a structured state space model named Mamba with multi-grained multi-modal interaction (SurvMamba) for survival prediction.<n>SurvMamba is implemented with a Hierarchical Interaction Mamba (HIM) module that facilitates efficient intra-modal interactions at different granularities.<n>An Interaction Fusion Mamba (IFM) module is used for cascaded inter-modal interactive fusion, yielding more comprehensive features for survival prediction.
  arXiv  Detail & Related papers  (2024-04-11T15:58:12Z)
• TTMFN: Two-stream Transformer-based Multimodal Fusion Network for Survival Prediction [7.646155781863875]
  We propose a novel framework named Two-stream Transformer-based Multimodal Fusion Network for survival prediction (TTMFN) In TTMFN, we present a two-stream multimodal co-attention transformer module to take full advantage of the complex relationships between different modalities. The experiment results on four datasets from The Cancer Genome Atlas demonstrate that TTMFN can achieve the best performance or competitive results.
  arXiv  Detail & Related papers  (2023-11-13T02:31:20Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities [76.08541852988536]
  We propose to use invariant features for a missing modality imagination network (IF-MMIN) We show that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions.
  arXiv  Detail & Related papers  (2022-10-27T12:16:25Z)
• A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completion [54.512440195060584]
  We propose the Unified Multi-Modal Conditional Score-based Generative Model (UMM-CSGM) to take advantage of Score-based Generative Model (SGM) UMM-CSGM employs a novel multi-in multi-out Conditional Score Network (mm-CSN) to learn a comprehensive set of cross-modal conditional distributions. Experiments on BraTS19 dataset show that the UMM-CSGM can more reliably synthesize the heterogeneous enhancement and irregular area in tumor-induced lesions.
  arXiv  Detail & Related papers  (2022-07-07T16:57:21Z)
• M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients [151.4352001822956]
  Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Existing prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume. We propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net)
  arXiv  Detail & Related papers  (2020-06-01T05:21:37Z)
• Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis [143.55901940771568]
  We propose a novel Hybrid-fusion Network (Hi-Net) for multi-modal MR image synthesis. In our Hi-Net, a modality-specific network is utilized to learn representations for each individual modality. A multi-modal synthesis network is designed to densely combine the latent representation with hierarchical features from each modality.
  arXiv  Detail & Related papers  (2020-02-11T08:26:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.