Neural Network Based Classification of Breast Cancer Histopathological Image from Intraoperative Rapid Frozen Sections.
Breast cancer is the leading cause of cancer-related mortality in women worldwide. Despite the rapid developments in diagnostic techniques and medical sciences, pathologic diagnosis is still recognized as the gold standard for disease diagnose. Pathologic diagnosis is a time-consuming task performed...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1597 - 1608 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2023
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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=169808797&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808797 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808797 162465445 169808797 169808797 10.1007/s10278-023-00802-3 169808797 ppf: 1597 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Neural Network Based Classification of Breast Cancer Histopathological Image from Intraoperative Rapid Frozen Sections. aug: au: Yuan, Jingping Zhu, Wenkang Li, Hui Yan, Dandan Shen, Shengnan affil: Department of Pathology, Renmin Hospital of Wuhan University, 430060, Wuhan, China sug: subj: Neural Networks (Computer) Breast Neoplasms Diagnosis Frozen Sections Methods Image Processing, Computer Assisted Classification Intraoperative Care Professional Knowledge Image Enhancement Pathologists ab: Breast cancer is the leading cause of cancer-related mortality in women worldwide. Despite the rapid developments in diagnostic techniques and medical sciences, pathologic diagnosis is still recognized as the gold standard for disease diagnose. Pathologic diagnosis is a time-consuming task performed for pathologists, needing profound professional knowledge and long-term accumulated diagnostic experience. Therefore, the development of automatic and precise histopathological image classification is essential for medical diagnosis. In this study, an improved VGG network was used to classify the breast cancer histopathological image from intraoperative rapid frozen sections. We adopt a transformed loss function by adding a penalty to cross-entropy in our training stage, which improved the accuracy on test data by 4.39%. Laplacian-4 was used for the enhancement of images, which contributes to the improvement of the accuracy. The accuracy of the proposed model on training data and test data reached 88.70% and 82.27%, respectively, which outperforms the original model by 9.39% of accuracy in test data. The process time was less than 0.25 s per image on average. Meanwhile, the heat maps of predictions were given to show the evidential regions in histopathological images, which could drive improvements in the accuracy, speed, and clinical value of pathological diagnoses. In addition to helping with the actual diagnosis, this technology may be a benefit to pathologists, surgeons, and patients. It might prove to be a helpful tool for pathologists in the future. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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