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Related Experiment Videos

MILA-MIL:Mamba-Inspired Linear Attention Multiple Instance Learning for Whole-Slide Image Survival Prediction.

Xinyu Li, Ziwei Hu, Xinlin Zhang

    IEEE Journal of Biomedical and Health Informatics
    |May 12, 2026
    PubMed
    Summary
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    This study introduces MILA-MIL, a novel framework for predicting cancer survival from whole-slide images. It effectively combines local details and global context, improving prognostic accuracy in precision oncology.

    Area of Science:

    • Computational pathology
    • Precision oncology
    • Artificial intelligence in medicine

    Background:

    • Survival prediction from Whole-Slide Images (WSIs) is crucial for precision oncology but challenged by gigapixel image scale and tumor heterogeneity.
    • Existing Multiple Instance Learning (MIL) methods struggle to balance local morphological details with long-range prognostic dependencies.

    Purpose of the Study:

    • To develop a novel dual-branch framework, MILA-MIL, to integrate local micro-anatomical features and global survival information for improved prognostic prediction.
    • To address the limitations of current MIL approaches in capturing both fine-grained pathological gradients and scalable long-range dependencies.

    Main Methods:

    • Proposed a novel dual-branch framework, MILA-MIL, incorporating a Pinwheel Convolution (P-Conv) module for directional morphological gradients and a Mamba-inspired Linear Attention (MILA) branch for efficient global context modeling.

    Related Experiment Videos

  • Utilized a gated fusion mechanism to dynamically integrate local and global representations.
  • Evaluated the framework on six diverse cancer cohorts.

    • Main Results:

    • MILA-MIL achieved state-of-the-art performance across six cancer cohorts, demonstrating superior stability and predictive power compared to existing MIL aggregators.
    • The framework effectively synergizes directional morphology with scalable global modeling for enhanced prognostic accuracy.

    Conclusions:

    • MILA-MIL offers a robust and interpretable solution for computational pathology, bridging local and global information for survival prediction.
    • This approach has significant potential to enhance clinical decision-making in personalized cancer management.