Multi-modal Intermediate Feature Interaction AutoEncoder for Overall Survival Prediction of Esophageal Squamous Cell Cancer

• URL: http://arxiv.org/abs/2408.13290v1
• Date: Fri, 23 Aug 2024 09:11:05 GMT
• Title: Multi-modal Intermediate Feature Interaction AutoEncoder for Overall Survival Prediction of Esophageal Squamous Cell Cancer
• Authors: Chengyu Wu, Yatao Zhang, Yaqi Wang, Qifeng Wang, Shuai Wang,
• Abstract summary: We propose a novel autoencoder-based deep learning model to predict the overall survival of the ESCC. Two novel modules were designed for multi-modal prognosis-related feature reinforcement and modeling ability enhancement. Our model can achieve satisfactory results in terms of discriminative ability, risk stratification, and the effectiveness of the proposed modules.
• Score: 8.183502190604687
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Survival prediction for esophageal squamous cell cancer (ESCC) is crucial for doctors to assess a patient's condition and tailor treatment plans. The application and development of multi-modal deep learning in this field have attracted attention in recent years. However, the prognostically relevant features between cross-modalities have not been further explored in previous studies, which could hinder the performance of the model. Furthermore, the inherent semantic gap between different modal feature representations is also ignored. In this work, we propose a novel autoencoder-based deep learning model to predict the overall survival of the ESCC. Two novel modules were designed for multi-modal prognosis-related feature reinforcement and modeling ability enhancement. In addition, a novel joint loss was proposed to make the multi-modal feature representations more aligned. Comparison and ablation experiments demonstrated that our model can achieve satisfactory results in terms of discriminative ability, risk stratification, and the effectiveness of the proposed modules.

Related papers

• Multi-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations [0.6990493129893112]
  M4Survive is a novel framework that learns joint foundation model representations using efficient adapter networks. By leveraging Mamba-based adapters, M4Survive enables efficient multi-modal learning while preserving computational efficiency. This work underscores the potential of foundation model-driven multi-modal fusion in advancing precision oncology and predictive analytics.
  arXiv  Detail & Related papers  (2025-03-13T05:18:32Z)
• MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention [52.106879463828044]
  Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease. We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention. Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
  arXiv  Detail & Related papers  (2025-03-01T07:02:30Z)
• Continually Evolved Multimodal Foundation Models for Cancer Prognosis [50.43145292874533]
  Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. Previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information. 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. 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)
• Survival Prediction in Lung Cancer through Multi-Modal Representation Learning [9.403446155541346]
  This paper presents a novel approach to survival prediction by harnessing comprehensive information from CT and PET scans, along with associated Genomic data. We aim to develop a robust predictive model for survival outcomes by integrating multi-modal imaging data with genetic information.
  arXiv  Detail & Related papers  (2024-09-30T10:42:20Z)
• MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment [20.358300924109162]
  In clinical practice, certain modalities of MRI may be missing, which presents a more difficult scenario. Knowledge Distillation, Domain Adaption, and Shared Latent Space have emerged as commonly promising strategies. We propose a novel paradigm that aligns latent features of involved modalities to a well-defined distribution anchor as the substitution of the pre-trained model.
  arXiv  Detail & Related papers  (2024-08-18T13:16:30Z)
• 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)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• 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)
• Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning [55.789874096142285]
  We present a deep learning-based brain tumor recurrence location prediction network. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location.
  arXiv  Detail & Related papers  (2023-04-11T02:45:38Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• A Twin Neural Model for Uplift [59.38563723706796]
  Uplift is a particular case of conditional treatment effect modeling. We propose a new loss function defined by leveraging a connection with the Bayesian interpretation of the relative risk. We show our proposed method is competitive with the state-of-the-art in simulation setting and on real data from large scale randomized experiments.
  arXiv  Detail & Related papers  (2021-05-11T16:02:39Z)
• DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their Interactions [2.30238915794052]
  We propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days. Using data collected from various sources, we estimate the range of increase in infected cases seven days into the future for all U.S. counties.
  arXiv  Detail & Related papers  (2020-07-31T23:37:38Z)

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.