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  1. Home
  3. Multi-view And Multi-scale Alignment For Contrastive Language-image Pre-training In Mammography.
  1. Home
  3. Multi-view And Multi-scale Alignment For Contrastive Language-image Pre-training In Mammography.

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Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography.

Yuexi Du1, John A Onofrey1,2,3, Nicha C Dvornek1,2

  • 1Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Information Processing in Medical Imaging : Proceedings of the ... Conference
|September 25, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We introduce a new method for mammography analysis using Contrastive Language-Image Pre-training (CLIP). Our approach addresses data limitations and improves performance on key tasks, offering a more efficient model for breast cancer screening.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Contrastive Language-Image Pre-training (CLIP) shows promise in medical imaging but requires extensive data and computational power.
  • Current CLIP applications are limited to data-rich modalities like chest X-rays, neglecting under-explored areas such as mammography.
  • Mammography presents unique challenges including scarce labeled data, high-resolution images with small regions of interest, and class imbalance.

Purpose of the Study:

  • To adapt the full CLIP model for mammography analysis, overcoming existing data and computational constraints.
  • To develop novel techniques for leveraging multi-view mammography data and enhancing focus on detailed features.
  • To address data limitations through parameter-efficient fine-tuning of medical knowledge-pretrained large language models.

Main Methods:

  • Developed a specialized supervision framework utilizing the multi-view nature of mammography.
  • Designed a symmetric local alignment module to improve focus on high-resolution image details.
  • Incorporated parameter-efficient fine-tuning for large language models with medical pre-training.

Main Results:

  • The proposed multi-view and multi-scale alignment (MaMA) method demonstrated superior performance across three distinct tasks.
  • Evaluated on the large-scale EMBED and RSNA-Mammo mammography datasets, MaMA outperformed state-of-the-art baselines.
  • Achieved comparable results with a significantly reduced model size (52% of the largest baseline).

Conclusions:

  • The MaMA method offers an effective and efficient adaptation of CLIP for mammography analysis.
  • This approach successfully tackles challenges like data scarcity and image complexity in mammography.
  • The findings suggest a promising direction for advancing AI in under-explored medical imaging modalities.