A multi‐stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four‐view mammograms.
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| Title: | A multi‐stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four‐view mammograms. |
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| Authors: | Jones, Meredith A.1 (AUTHOR) meredith.jones@ou.edu, Sadeghipour, Negar2 (AUTHOR), Chen, Xuxin2 (AUTHOR), Islam, Warid2 (AUTHOR), Zheng, Bin2 (AUTHOR) |
| Source: | Medical Physics. Dec2023, Vol. 50 Issue 12, p7670-7683. 14p. |
| Subjects: | Deep learning, Radiomics, Computer-aided diagnosis, Mammograms, Feature extraction, Breast, Image fusion |
| Abstract: | Background: Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnostic decisions based on the fusion of image features extracted from multi‐view mammograms, most CAD schemes are single‐view‐based schemes, which limit CAD performance and clinical utility. Purpose: This study aims to develop and test a novel CAD framework that optimally fuses information extracted from ipsilateral views of bilateral mammograms using both deep transfer learning (DTL) and radiomics feature extraction methods. Methods: An image dataset containing 353 benign and 611 malignant cases is assembled. Each case contains four images: the craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breast. First, we extract four matching regions of interest (ROIs) from images that surround centers of two suspicious lesion regions seen in CC and MLO views, as well as matching ROIs in the contralateral breasts. Next, the handcrafted radiomics (HCRs) features and VGG16 model‐generated automated features are extracted from each ROI resulting in eight feature vectors. Then, after reducing feature dimensionality and quantifying the bilateral and ipsilateral asymmetry of four ROIs to yield four new feature vectors, we test four fusion methods to build three support vector machine (SVM) classifiers by an optimal fusion of asymmetrical image features extracted from four view images. Results: Using a 10‐fold cross‐validation method, results show that a SVM classifier trained using an optimal fusion of four view images yields the highest classification performance (AUC = 0.876 ± 0.031), which significantly outperforms SVM classifiers trained using one projection view alone, AUC = 0.817 ± 0.026 and 0.792 ± 0.026 for the CC and MLO view of bilateral mammograms, respectively (p < 0.001). Conclusions: The study demonstrates that the shift from single‐view CAD to four‐view CAD and the inclusion of both DTL and radiomics features significantly increases CAD performance in distinguishing between malignant and benign breast lesions. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: A multi‐stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four‐view mammograms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jones%2C+Meredith+A%2E%22">Jones, Meredith A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> meredith.jones@ou.edu</i><br /><searchLink fieldCode="AR" term="%22Sadeghipour%2C+Negar%22">Sadeghipour, Negar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Xuxin%22">Chen, Xuxin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Islam%2C+Warid%22">Islam, Warid</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Bin%22">Zheng, Bin</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Dec2023, Vol. 50 Issue 12, p7670-7683. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Radiomics%22">Radiomics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-aided+diagnosis%22">Computer-aided diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Mammograms%22">Mammograms</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Breast%22">Breast</searchLink><br /><searchLink fieldCode="DE" term="%22Image+fusion%22">Image fusion</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnostic decisions based on the fusion of image features extracted from multi‐view mammograms, most CAD schemes are single‐view‐based schemes, which limit CAD performance and clinical utility. Purpose: This study aims to develop and test a novel CAD framework that optimally fuses information extracted from ipsilateral views of bilateral mammograms using both deep transfer learning (DTL) and radiomics feature extraction methods. Methods: An image dataset containing 353 benign and 611 malignant cases is assembled. Each case contains four images: the craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breast. First, we extract four matching regions of interest (ROIs) from images that surround centers of two suspicious lesion regions seen in CC and MLO views, as well as matching ROIs in the contralateral breasts. Next, the handcrafted radiomics (HCRs) features and VGG16 model‐generated automated features are extracted from each ROI resulting in eight feature vectors. Then, after reducing feature dimensionality and quantifying the bilateral and ipsilateral asymmetry of four ROIs to yield four new feature vectors, we test four fusion methods to build three support vector machine (SVM) classifiers by an optimal fusion of asymmetrical image features extracted from four view images. Results: Using a 10‐fold cross‐validation method, results show that a SVM classifier trained using an optimal fusion of four view images yields the highest classification performance (AUC = 0.876 ± 0.031), which significantly outperforms SVM classifiers trained using one projection view alone, AUC = 0.817 ± 0.026 and 0.792 ± 0.026 for the CC and MLO view of bilateral mammograms, respectively (p < 0.001). Conclusions: The study demonstrates that the shift from single‐view CAD to four‐view CAD and the inclusion of both DTL and radiomics features significantly increases CAD performance in distinguishing between malignant and benign breast lesions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mp.16419 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 7670 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Radiomics Type: general – SubjectFull: Computer-aided diagnosis Type: general – SubjectFull: Mammograms Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Breast Type: general – SubjectFull: Image fusion Type: general Titles: – TitleFull: A multi‐stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four‐view mammograms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jones, Meredith A. – PersonEntity: Name: NameFull: Sadeghipour, Negar – PersonEntity: Name: NameFull: Chen, Xuxin – PersonEntity: Name: NameFull: Islam, Warid – PersonEntity: Name: NameFull: Zheng, Bin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 50 – Type: issue Value: 12 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
