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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Quality control system for mammographic breast positioning using deep learning.

Haruyuki Watanabe1, Saeko Hayashi2, Yohan Kondo3

  • 1School of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan. hal-watanabe@gchs.ac.jp.

Scientific Reports
|May 1, 2023
PubMed
Summary
This summary is machine-generated.

A deep convolutional neural network (DCNN) accurately classifies mammography breast positioning quality. This automated method improves quality control, reducing the need for manual evaluations in mammographic screening.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Mammography quality control is crucial for accurate breast cancer detection.
  • Manual evaluation of breast positioning can be subjective and time-consuming.

Purpose of the Study:

  • To develop and validate a deep convolutional neural network (DCNN) for automated classification of breast positioning quality in mammography.
  • To quantitatively assess mammographic positioning accuracy using DCNNs.

Main Methods:

  • Collected 1631 mediolateral oblique mammographic views from an open database.
  • Implemented a two-step process: automated region of interest detection and DCNN-based classification of positioning quality into three scales.
  • Evaluated four representative DCNN models (VGG16, Xception).

Main Results:

  • Achieved a best positioning classification accuracy of 0.7836 using VGG16 for the inframammary fold.
  • Attained a classification accuracy of 0.7278 using Xception for the nipple profile.
  • Demonstrated quantitative evaluation of breast positioning criteria using softmax probabilities.

Conclusions:

  • The proposed DCNN method offers an automated and quantitative approach to mammography quality control.
  • This technique has the potential to enhance the reliability and efficiency of breast positioning validation in mammography.