Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers.
Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such steps in digital mammography screening for breast cancer is the labeling and removal of special diagnostic views, in which diagnostic tools or magnificat...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 2; pp. 228 - 234 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2019
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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=135821490&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135821490 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2019 vid: 32 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135821490 135821490 135821490 10.1007/s10278-018-0154-z 135821490 ppf: 228 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers. aug: au: Lituiev, Dmytro S. Trivedi, Hari Panahiazar, Maryam Norgeot, Beau Seo, Youngho Franc, Benjamin Harnish, Roy Kawczynski, Michael Hadley, Dexter affil: Institute for Computational Health Sciences, University of California, San Francisco, 550 16th Street, San Francisco, CA, USA sug: subj: Breast Neoplasms Radiography Mammography Methods DICOM Machine Learning Image Processing, Computer Assisted Methods Radiographic Image Enhancement Human Female Radiographic Image Interpretation, Computer-Assisted Descriptive Statistics Sensitivity and Specificity Algorithms Female ab: Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such steps in digital mammography screening for breast cancer is the labeling and removal of special diagnostic views, in which diagnostic tools or magnification are applied to assist in assessment of suspicious initial findings. As a common task in medical informatics is prediction of disease and its stage, these special diagnostic views, which are only enriched among the cohort of diseased cases, will bias machine learning disease predictions. In order to automate this process, here, we develop a machine learning pipeline that utilizes both DICOM headers and images to predict such views in an automatic manner, allowing for their removal and the generation of unbiased datasets. We achieve AUC of 99.72% in predicting special mammogram views when combining both types of models. Finally, we apply these models to clean up a dataset of about 772,000 images with expected sensitivity of 99.0%. The pipeline presented in this paper can be applied to other datasets to obtain high-quality image sets suitable to train algorithms for disease detection. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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