A Novel Structure Fusion Attention Model to Detect Architectural Distortion on Mammography.
Architectural distortion (AD) is one of the most common findings on mammograms, and it may represent not only cancer but also a lesion such as a radial scar that may have an associated cancer. AD accounts for 18–45% missed cancer, and the positive predictive value of AD is approximately 74.5%. Early...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2227 - 2252 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515397&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515397 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515397 181515397 181515397 10.1007/s10278-024-01085-y 181515397 ppf: 2227 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Structure Fusion Attention Model to Detect Architectural Distortion on Mammography. aug: au: Ou, Ting-Wei Weng, Tzu-Chieh Chang, Ruey-Feng affil: https://ror.org/05bqach95 Department of Medicine, National Taiwan University, Taipei, Taiwan sug: subj: Breast Neoplasms Radiography Breast Neoplasms Pathology Mammography Methods Radiographic Image Interpretation, Computer-Assisted Human Female Breast Physiopathology Sensitivity and Specificity Magnetic Resonance Imaging Breast Neoplasms Prognosis Early Detection of Cancer Methods Deep Learning Descriptive Statistics Female ab: Architectural distortion (AD) is one of the most common findings on mammograms, and it may represent not only cancer but also a lesion such as a radial scar that may have an associated cancer. AD accounts for 18–45% missed cancer, and the positive predictive value of AD is approximately 74.5%. Early detection of AD leads to early diagnosis and treatment of the cancer and improves the overall prognosis. However, detection of AD is a challenging task. In this work, we propose a new approach for detecting architectural distortion in mammography images by combining preprocessing methods and a novel structure fusion attention model. The proposed structure-focused weighted orientation preprocessing method is composed of the original image, the architecture enhancement map, and the weighted orientation map, highlighting suspicious AD locations. The proposed structure fusion attention model captures the information from different channels and outperforms other models in terms of false positives and top sensitivity, which refers to the maximum sensitivity that a model can achieve under the acceptance of the highest number of false positives, reaching 0.92 top sensitivity with only 0.6590 false positive per image. The findings suggest that the combination of preprocessing methods and a novel network architecture can lead to more accurate and reliable AD detection. Overall, the proposed approach offers a novel perspective on detecting ADs, and we believe that our method can be applied to clinical settings in the future, assisting radiologists in the early detection of ADs from mammography, ultimately leading to early treatment of breast cancer patients. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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