Weakly-supervised deep learning for ultrasound diagnosis of breast cancer

Conventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We aimed to develop a weakly-supervised DL algorithm that diagnosis breast cancer at ultrasound without image annotation. Weakly-supervised DL algorithms...

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Bibliographic Details
Published in:	Scientific reports Vol. 11; no. 1; pp. 24382 - 10
Main Authors:	Kim, Jaeil, Kim, Hye Jung, Kim, Chanho, Lee, Jin Hwa, Kim, Keum Won, Park, Young Mi, Kim, Hye Won, Ki, So Yeon, Kim, You Me, Kim, Won Hwa
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 21.12.2021 Nature Publishing Group Nature Portfolio
Subjects:	631/67 631/67/1347 692/4028 692/700 692/700/1421/1860 Adolescent Adult Aged Aged, 80 and over Algorithms Annotations Automation Breast - diagnostic imaging Breast - pathology Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - diagnostic imaging Case-Control Studies Deep Learning Diagnosis, Differential Differential diagnosis Female Follow-Up Studies Humanities and Social Sciences Humans Image Interpretation, Computer-Assisted - methods Localization Middle Aged multidisciplinary Neural Networks, Computer Prognosis Retrospective Studies ROC Curve Science Science (multidisciplinary) Ultrasonic imaging Ultrasonography, Mammary - methods Ultrasound Young Adult United States > US
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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