Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels

Key points

• Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets.1
• Conventional machine learning approaches often fail to extract meaningful cross-modal features, thereby limiting predictive accuracy in patient risk stratification.1
• The framework employs tensor decomposition to fuse heterogeneous genomic and clinical data, enabling the extraction of latent, biologically meaningful features that outperform unimodal feature sets.1
• For patient risk stratification, clinically relevant attributes such as tumor purity, pathological stage, and survival outcomes serve as ground-truth indicators.1

Sources (1)

• [1]Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 08:37 AM

  To address these limitations, this paper proposes an unsupervised machine learning framework that integrates feature extraction, tensor decomposition, and clustering to improve cancer risk prediction.

  Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets.

Extractive summary: sentences quoted from the sources.