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Updated: Apr 14, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

A multi-modal survival prediction framework with group-based batch training and structural consistency alignment.

Wei Li¹, Xiangyu Tan¹

¹School of Automation, Harbin University of Science and Technology, Harbin 150030, China.

Journal of Biomedical Informatics

|April 12, 2026

Summary

This summary is machine-generated.

PRISM enhances cancer survival prediction by integrating whole-slide images and transcriptomic data efficiently. This novel framework improves prognostic accuracy while significantly reducing computational costs for multimodal learning.

Keywords:

Computational pathology Cross-modal alignment Multimodal learning Survival prediction Transcriptomics Whole-slide images

Related Experiment Videos

Last Updated: Apr 14, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Computational pathology
Bioinformatics
Machine learning for healthcare

Background:

Integrating whole-slide images (WSIs) and transcriptomic profiles is crucial for improving cancer survival prediction.
Existing methods face challenges in balancing training efficiency and data heterogeneity due to WSI gigapixel resolution and variable sequence lengths.
Discrepancies between histological and genomic data hinder effective cross-modal alignment and fusion, limiting prognostic accuracy.

Purpose of the Study:

To introduce PRISM, an efficient multi-modal learning framework for integrating WSIs with transcriptomic profiles.
To address the conflict between training efficiency and data heterogeneity preservation in existing frameworks.
To improve cross-modal alignment and fusion for enhanced cancer survival prediction.

Main Methods:

PRISM employs a novel stochastic partitioning strategy to divide WSIs into main and residual subsets for efficient batch training.
It utilizes isolation masking in the main branch and a residual branch with tailored supervision to capture intra- and inter-slide correlations.
Key modules include informative token aggregation for WSI redundancy reduction and Low-rank Bilinear Gating Fusion for efficient cross-modal interaction.

Main Results:

PRISM achieved superior overall C-index across five TCGA cohorts compared to existing methods.
On the TCGA-BRCA dataset, PRISM demonstrated significantly reduced training time (6 hours) compared to strong multimodal baselines.
The framework showed the best overall Integrated Brier Score (IBS) ranking and favorable time-dependent AUC performance at 1, 3, and 5 years.

Conclusions:

PRISM offers an effective balance between predictive performance, calibration, and computational efficiency.
The framework shows significant potential for practical multimodal survival modeling in computational pathology.
PRISM represents a advancement in leveraging integrated histopathology and genomics for cancer prognosis.