Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms.
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 6; pp. 1414 - 1424 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2021
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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=154097231&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154097231 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2021 vid: 34 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154097231 153367425 154097231 154097231 10.1007/s10278-021-00530-6 154097231 ppf: 1414 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms. aug: au: Wu, Nan Huang, Zhe Shen, Yiqiu Park, Jungkyu Phang, Jason Makino, Taro Gene Kim, S. Cho, Kyunghyun Heacock, Laura Moy, Linda Geras, Krzysztof J. affil: Center for Data Science, New York University, New York City, USA sug: subj: False Positive Results Neural Networks (Computer) Biopsy Mammography Breast Neoplasms Radiography Cancer Screening Human Radiologists Image Processing, Computer Assisted Deep Learning Breast Neoplasms Classification ab: Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign, with the goal of using these networks as second readers serving radiologists to further reduce the number of false-positive findings. We enhance the performance of DNNs that are trained to learn from small image patches by integrating global context provided in the form of saliency maps learned from the entire image into their reasoning, similar to how radiologists consider global context when evaluating areas of interest. Our experiments are conducted on a dataset of 229,426 screening mammography examinations from 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464 benign and 136 malignant lesions. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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