Machine learning approaches for pathologic diagnosis.
Machine learning techniques, especially deep learning techniques such as convolutional neural networks, have been successfully applied to general image recognitions since their overwhelming performance at the 2012 ImageNet Large Scale Visual Recognition Challenge. Recently, such techniques have also...
| Publicado en: | Virchows Archiv: European Journal of Pathology Vol. 475; no. 2; pp. 131 - 139 |
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| Autores principales: | , |
| Formato: | review Journal Article |
| Publicado: |
Springer Nature
Aug2019
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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=137642240&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137642240 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09456317 O1Z jtl: Virchows Archiv: European Journal of Pathology issn: 09456317 maglogo: N pubinfo: dt: Aug2019 vid: 475 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137642240 137642240 NLM31222375 137642240 10.1007/s00428-019-02594-w NLM31222375 137642240 ppf: 131 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning approaches for pathologic diagnosis. aug: au: Komura, Daisuke Ishikawa, Shumpei affil: Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-0033, Tokyo, Japan sug: subj: Pathology, Clinical Methods Questionnaires Ways of Coping Questionnaire ab: Machine learning techniques, especially deep learning techniques such as convolutional neural networks, have been successfully applied to general image recognitions since their overwhelming performance at the 2012 ImageNet Large Scale Visual Recognition Challenge. Recently, such techniques have also been applied to various medical, including histopathological, images to assist the process of medical diagnosis. In some cases, deep learning-based algorithms have already outperformed experienced pathologists for recognition of histopathological images. However, pathological images differ from general images in some aspects, and thus, machine learning of histopathological images requires specialized learning methods. Moreover, many pathologists are skeptical about the ability of deep learning technology to accurately recognize histopathological images because what the learned neural network recognizes is often indecipherable to humans. In this review, we first introduce various applications incorporating machine learning developed to assist the process of pathologic diagnosis, and then describe machine learning problems related to histopathological image analysis, and review potential ways to solve these problems. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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