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  1. Home
  3. Mil-adapter: Coupling Multiple Instance Learning And Vision-language Adapters For Few-shot Slide-level Classification.
  1. Home
  3. Mil-adapter: Coupling Multiple Instance Learning And Vision-language Adapters For Few-shot Slide-level Classification.

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MIL-Adapter: Coupling multiple instance learning and vision-language adapters for few-shot slide-level

Pablo Meseguer1, Rocío Del Amor2, Valery Naranjo2

  • 1organization=Instituto Universitario de Investigación e Innovación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, city=Valencia, country=Spain.

Medical Image Analysis
|January 30, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

MIL-Adapter enhances computational pathology by combining multiple instance learning (MIL) with vision-language models (VLMs) for accurate few-shot slide classification. This approach improves cancer diagnosis by leveraging textual ensemble learning for better zero-shot predictions.

Keywords:
Few-shot learningMultiple instance learningSlide-level classificationTextual ensemble learningVision-language adapters

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Area of Science:

  • Computational Pathology
  • Artificial Intelligence
  • Computer Vision

Background:

  • Vision-language models (VLMs) excel in visual representation and zero-shot classification.
  • Multiple instance learning (MIL) is crucial for handling large whole-slide images (WSIs) in computational pathology (CPath).
  • Existing VLMs lack specialized adapters for the complexities of histopathological slides.

Purpose of the Study:

  • To introduce MIL-Adapter, a novel framework for consistent slide-level classification in few-shot learning scenarios within CPath.
  • To address the gap in specialized adapters for VLMs in histopathology.
  • To improve the performance of VLMs in CPath using MIL and adapter techniques.

Main Methods:

  • Developed MIL-Adapter, integrating trainable MIL aggregation functions with lightweight visual-language adapters.
  • Employed textual ensemble learning to construct discriminative zero-shot prototypes.
  • Evaluated the framework on diverse datasets across various few-shot learning configurations.

    • Main Results:

    • MIL-Adapter demonstrated superior performance compared to baseline MIL models in data-constrained settings.
    • The study validated the effectiveness of textual ensemble learning for prototype construction.
    • Robust predictive performance was observed across different datasets and few-shot scenarios.

    Conclusions:

    • MIL-Adapter provides a strong foundation for few-shot learning in computational pathology.
    • The framework successfully combines MIL and VLMs for enhanced histopathological image analysis.
    • The approach offers valuable insights into model interpretability and performance in few-shot settings.