Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection
Abstract Employing two standard mammography views is crucial for radiologists, providing comprehensive insights for reliable clinical evaluations. This study introduces paired mammogram view based-network(PMVnet), a novel algorithm designed to enhance breast lesion detection by integrating relationa...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88907-3 |
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| author | Jae Won Seo Young Jae Kim Kwang Gi Kim |
| author_facet | Jae Won Seo Young Jae Kim Kwang Gi Kim |
| author_sort | Jae Won Seo |
| collection | DOAJ |
| description | Abstract Employing two standard mammography views is crucial for radiologists, providing comprehensive insights for reliable clinical evaluations. This study introduces paired mammogram view based-network(PMVnet), a novel algorithm designed to enhance breast lesion detection by integrating relational information from paired whole mammograms, addressing the limitations of current methods. Utilizing 1,636 private mammograms, PMVnet combines cosine similarity and the squeeze-and-excitation method within a U-shaped architecture to leverage correlated information. Performance comparisons with single view-based models with VGGnet16, Resnet50, and EfficientnetB5 as encoders revealed PMVnet’s superior capability. Using VGGnet16, PMVnet achieved a Dice Similarity Coefficient (DSC) of 0.709 in segmentation and a recall of 0.950 at 0.156 false positives per image (FPPI) in detection tasks, outperforming the single-view model, which had a DSC of 0.579 and a recall of 0.813 at 0.188 FPPI. These findings demonstrate PMVnet’s effectiveness in reducing false positives and avoiding missed true positives, suggesting its potential as a practical tool in computer-aided diagnosis systems. PMVnet can significantly enhance breast lesion detection, aiding radiologists in making more precise evaluations and improving patient outcomes. Future applications of PMVnet may offer substantial benefits in clinical settings, improving patient care through enhanced diagnostic accuracy. |
| format | Article |
| id | doaj-art-9f38d00df6d2469b9c786961c6538dbd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-9f38d00df6d2469b9c786961c6538dbd2025-02-09T12:36:16ZengNature PortfolioScientific Reports2045-23222025-02-0115111310.1038/s41598-025-88907-3Leveraging paired mammogram views with deep learning for comprehensive breast cancer detectionJae Won Seo0Young Jae Kim1Kwang Gi Kim2Department of Health Sciences and Technology, GAIHST, Gachon UniversityDepartment of Gachon Biomedical & Convergence Institute, Gachon University Gil Medical CenterDepartment of Health Sciences and Technology, GAIHST, Gachon UniversityAbstract Employing two standard mammography views is crucial for radiologists, providing comprehensive insights for reliable clinical evaluations. This study introduces paired mammogram view based-network(PMVnet), a novel algorithm designed to enhance breast lesion detection by integrating relational information from paired whole mammograms, addressing the limitations of current methods. Utilizing 1,636 private mammograms, PMVnet combines cosine similarity and the squeeze-and-excitation method within a U-shaped architecture to leverage correlated information. Performance comparisons with single view-based models with VGGnet16, Resnet50, and EfficientnetB5 as encoders revealed PMVnet’s superior capability. Using VGGnet16, PMVnet achieved a Dice Similarity Coefficient (DSC) of 0.709 in segmentation and a recall of 0.950 at 0.156 false positives per image (FPPI) in detection tasks, outperforming the single-view model, which had a DSC of 0.579 and a recall of 0.813 at 0.188 FPPI. These findings demonstrate PMVnet’s effectiveness in reducing false positives and avoiding missed true positives, suggesting its potential as a practical tool in computer-aided diagnosis systems. PMVnet can significantly enhance breast lesion detection, aiding radiologists in making more precise evaluations and improving patient outcomes. Future applications of PMVnet may offer substantial benefits in clinical settings, improving patient care through enhanced diagnostic accuracy.https://doi.org/10.1038/s41598-025-88907-3MammogramBreastArtificial intelligenceDetectionComputer-aided diagnostic system |
| spellingShingle | Jae Won Seo Young Jae Kim Kwang Gi Kim Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection Scientific Reports Mammogram Breast Artificial intelligence Detection Computer-aided diagnostic system |
| title | Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection |
| title_full | Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection |
| title_fullStr | Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection |
| title_full_unstemmed | Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection |
| title_short | Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection |
| title_sort | leveraging paired mammogram views with deep learning for comprehensive breast cancer detection |
| topic | Mammogram Breast Artificial intelligence Detection Computer-aided diagnostic system |
| url | https://doi.org/10.1038/s41598-025-88907-3 |
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