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Related Experiment Video

Updated: Dec 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Mammography Image Quality Assurance Using Deep Learning.

Tobias Kretz, Klaus-Robert Mueller, Tobias Schaeffter

    IEEE Transactions on Bio-Medical Engineering
    |April 20, 2020
    PubMed
    Summary
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    This study introduces a deep learning framework for mammography image quality assessment. This AI approach provides reliable image quality predictions using single images, offering an alternative to current multi-image methods.

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Radiology

    Background:

    • Current mammography image quality assessment relies on the European Reference Organization for Quality Assured Breast Cancer Screening and Diagnostic Services (EUREF) procedure, which involves analyzing multiple images of a CDMAM phantom.
    • The EUREF procedure utilizes automated analysis including image registration, signal detection, and nonlinear fitting, which can be complex and time-consuming.

    Purpose of the Study:

    • To present a proof-of-concept for an end-to-end deep learning framework as an alternative method for assessing mammography image quality.
    • To develop a deep learning model capable of evaluating image quality from single mammographic images.

    Main Methods:

    • A regression convolutional neural network (CNN) was trained using a database of virtual mammography images with known ground truth.

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  • The training process involved continuous data extension and transfer learning techniques.
  • The developed deep learning framework was tested on both simulated and real mammographic images.

    • Main Results:

    • The trained CNN accurately predicted the image quality of simulated and real mammographic images.
    • Image quality predictions using single images from the deep learning model were comparable in quality to those obtained using the EUREF procedure with 16 images.
    • The study demonstrated that the trained CNN exhibits good generalization capabilities.

    Conclusions:

    • The proposed deep learning approach offers a promising alternative for mammography image quality assessment.
    • This AI-driven method simplifies the assessment process by eliminating the need for cumbersome pre-processing and enabling reliable quality estimation from single images.