ArticleScientific reports2023
Quality control system for mammographic breast positioning using deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Comparison of two automated PGMI-based software tools for breast positioning quality assessment in DBT screening.European radiology experimental · 2026Article
- U-Net-Based Automated Quality Control of Knee Radiographs: Dual-Center Validation and Clinical Intervention.Journal of imaging informatics in medicine · 2026Article
- Landmark Regression with Attention U-Net for Assessing Breast Positioning Quality in MLO Mammography View.Diagnostics (Basel, Switzerland) · 2026Article
- From Standard Establishment to Precision Intelligence: Research Progress in Quality Control of Mammography.Diagnostics (Basel, Switzerland) · 2026Review
- Enhancing breast positioning quality through real-time AI feedback.European radiology · 2026Article
- Identifying key factors in the mammography procedure for delineating the pectoral muscle using the analytic hierarchy process.Scientific reports · 2025Article
- Factors associated with false-positive screening mammography in São Paulo, Brazil.Scientific reports · 2025Article
- Article
- Automated angular measurement for puncture angle using a computer-aided method in ultrasound-guided peripheral insertion.Physical and engineering sciences in medicine · 2024Article
- Automatic classification of symmetry of hemithoraces in canine and feline radiographs.Journal of medical imaging (Bellingham, Wash.) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposes a deep convolutional neural network (DCNN) classification for the quality control and validation of breast positioning criteria in mammography. A total of 1631 mediolateral oblique mammographic views were collected from an open database. We designed two main steps for mammographic verification: automated detection of the positioning part and classification of three scales that determine the positioning quality using DCNNs. After acquiring labeled mammograms with three scales visually evaluated based on guidelines, the first step was automatically detecting the region of interest of the subject part by image processing. The next step was classifying mammographic positioning accuracy into three scales using four representative DCNNs. The experimental results showed that the DCNN model achieved the best positioning classification accuracy of 0.7836 using VGG16 in the inframammary fold and a classification accuracy of 0.7278 using Xception in the nipple profile. Furthermore, using the softmax function, the breast positioning criteria could be evaluated quantitatively by presenting the predicted value, which is the probability of determining positioning accuracy. The proposed method can be quantitatively evaluated without the need for an individual qualitative evaluation and has the potential to improve the quality control and validation of breast positioning criteria in mammography.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.