Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations

• URL: http://arxiv.org/abs/2407.17726v1
• Date: Thu, 25 Jul 2024 02:55:39 GMT
• Title: Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations
• Authors: Linhao Qu, Dan Huang, Shaoting Zhang, Xiaosong Wang,
• Abstract summary: We introduce a novel framework that simultaneously handles incomplete data across modalities and censored survival labels. Our approach employs advanced foundation models to encode individual modalities and align them into a universal representation space. The proposed method demonstrates outstanding prediction accuracy in two survival analysis tasks on both employed datasets.
• Score: 19.560652381770243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Survival analysis stands as a pivotal process in cancer treatment research, crucial for predicting patient survival rates accurately. Recent advancements in data collection techniques have paved the way for enhancing survival predictions by integrating information from multiple modalities. However, real-world scenarios often present challenges with incomplete data, particularly when dealing with censored survival labels. Prior works have addressed missing modalities but have overlooked incomplete labels, which can introduce bias and limit model efficacy. To bridge this gap, we introduce a novel framework that simultaneously handles incomplete data across modalities and censored survival labels. Our approach employs advanced foundation models to encode individual modalities and align them into a universal representation space for seamless fusion. By generating pseudo labels and incorporating uncertainty, we significantly enhance predictive accuracy. The proposed method demonstrates outstanding prediction accuracy in two survival analysis tasks on both employed datasets. This innovative approach overcomes limitations associated with disparate modalities and improves the feasibility of comprehensive survival analysis using multiple large foundation models.

Related papers

• Conditioning on Time is All You Need for Synthetic Survival Data Generation [16.401141867387324]
  We propose a simple paradigm to produce synthetic survival data by generating co variables conditioned on event times and censoring indicators. Our methodology outperforms multiple competitive baselines at generating survival data, while improving the performance of downstream survival models trained on it and tested on real data.
  arXiv  Detail & Related papers  (2024-05-27T16:34:18Z)
• 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)
• DAGnosis: Localized Identification of Data Inconsistencies using Structures [73.39285449012255]
  Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models. We use directed acyclic graphs (DAGs) to encode the training set's features probability distribution and independencies as a structure. Our method, called DAGnosis, leverages these structural interactions to bring valuable and insightful data-centric conclusions.
  arXiv  Detail & Related papers  (2024-02-26T11:29:16Z)
• SurvivalGAN: Generating Time-to-Event Data for Survival Analysis [121.84429525403694]
  Imbalances in censoring and time horizons cause generative models to experience three new failure modes specific to survival analysis. We propose SurvivalGAN, a generative model that handles survival data by addressing the imbalance in the censoring and event horizons. We evaluate this method via extensive experiments on medical datasets.
  arXiv  Detail & Related papers  (2023-02-24T17:03:51Z)
• Heterogeneous Datasets for Federated Survival Analysis Simulation [6.489759672413373]
  This work proposes a novel technique for constructing realistic heterogeneous datasets by starting from existing non-federated datasets in a reproducible way. Specifically, we provide two novel dataset-splitting algorithms based on the Dirichlet distribution to assign each data sample to a carefully chosen client. The implementation of the proposed methods is publicly available in favor of and to encourage common practices to simulate federated environments for survival analysis.
  arXiv  Detail & Related papers  (2023-01-28T11:37:07Z)
• Predicting Survival Outcomes in the Presence of Unlabeled Data [0.0]
  We investigate whether we can benefit from the inclusion of such unlabeled data instances to predict accurate survival times. We propose three approaches to deal with this novel setting and provide an empirical comparison over fifteen real-life clinical and gene expression survival datasets.
  arXiv  Detail & Related papers  (2022-10-25T10:19:45Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• A Deep Variational Approach to Clustering Survival Data [5.871238645229228]
  We introduce a novel probabilistic approach to cluster survival data in a variational deep clustering setting. Our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and the potentially censored survival times.
  arXiv  Detail & Related papers  (2021-06-10T14:10:25Z)
• Towards Handling Uncertainty-at-Source in AI -- A Review and Next Steps for Interval Regression [6.166295570030645]
  This paper focuses on linear regression for interval-valued data as a recent growth area. We conduct an in-depth analysis of state-of-the-art methods, elucidating their behaviour, advantages, and pitfalls when applied to datasets with different properties.
  arXiv  Detail & Related papers  (2021-04-15T05:31:10Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

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.