{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T17:56:34Z","timestamp":1730310994314,"version":"3.28.0"},"reference-count":16,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,4]]},"DOI":"10.1117\/12.2612405","type":"proceedings-article","created":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T17:16:37Z","timestamp":1645204597000},"page":"36","source":"Crossref","is-referenced-by-count":0,"title":["Case-based repeatability and operating point variability of AI: breast lesion classification based on deep transfer learning"],"prefix":"10.1117","author":[{"given":"Heather M.","family":"Whitney","sequence":"first","affiliation":[]},{"given":"Karen","family":"Drukker","sequence":"additional","affiliation":[]},{"given":"Hiroyuki","family":"Abe","sequence":"additional","affiliation":[]},{"given":"Maryellen L.","family":"Giger","sequence":"additional","affiliation":[]}],"member":"189","reference":[{"issue":"March","key":"R1","first-page":"70","article-title":"Case-based repeatability of machine learning classification performance on breast MRI","volume":"1131421","author":"Vieceli","year":"2020"},{"issue":"March","key":"R2","first-page":"16","article-title":"Repeatability profiles towards consistent sensitivity and specificity levels for machine learning on breast DCE-MRI","volume":"11316","author":"van Dusen","year":"2020"},{"key":"R3","first-page":"115990O","article-title":"Comparison of diagnostic performances, case-based repeatability, and operating sensitivity and specificity in classification of breast lesions using DCE-MRI","volume-title":"Proc. SPIE 11599, Medical Imaging: Image Perception, Observer Performance, and Technology Assessment","author":"de Oliveira","year":"2021"},{"key":"R4","doi-asserted-by":"crossref","unstructured":"Amstutz, P., Drukker, K., Li, H., Abe, H., Giger, M. L. and Whitney, H. M., \u201cCase-based diagnostic classification repeatability using radiomic features extracted from full-field digital mammography images of breast lesions,\u201d Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970X (2021).","DOI":"10.1117\/12.2580743"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.1118\/1.3427409"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.3.3.034501"},{"issue":"1","key":"R7","first-page":"163","article-title":"Comparison of Breast MRI Tumor Classification Using Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods","volume-title":"Proceedings of the IEEE","volume":"108","author":"Whitney","year":"2020"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-67441-4"},{"issue":"3","key":"R9","first-page":"e200159","article-title":"Improved Classification of Benign and Malignant Breast Lesions Using Deep Feature Maximum Intensity Projection MRI in Breast Cancer Diagnosis Using Dynamic Contrast-enhanced MRI,\u201d Radiology","volume":"3","author":"Hu","year":"2021","journal-title":"Artificial Intelligence"},{"key":"R10","article-title":"Use of Deep Learning in the Classification of Benign Lesions, Luminal A Cancers, and Other Molecular Cancer Subtypes in Breast","volume-title":"Magnetic Resonance Imaging,\u201d","author":"Whitney","year":"2018"},{"article-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","year":"2014","author":"Simonyan","key":"R11"},{"key":"R12","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1109\/CVPR.2009.5206848","article-title":"ImageNet: A large-scale hierarchical image database","volume-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","author":"Deng","year":"2009"},{"issue":"438","key":"R13","first-page":"548","article-title":"Improvements on cross-validation: The. 632+ bootstrap method","volume":"92","author":"Efron","year":"1997","journal-title":"Journal of the American Statistical Association"},{"key":"R14","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2008.04.022"},{"key":"R15","doi-asserted-by":"publisher","DOI":"10.1118\/1.2868757"},{"key":"R16","article-title":"Statistical Methods in","volume-title":"Diagnostic Medicine","author":"Zhou","year":"2011"}],"event":{"name":"Image Perception, Observer Performance, and Technology Assessment","start":{"date-parts":[[2022,2,20]]},"location":"San Diego, United States","end":{"date-parts":[[2022,3,28]]}},"container-title":["Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment"],"original-title":[],"deposited":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T01:33:39Z","timestamp":1656812019000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12035\/2612405\/Case-based-repeatability-and-operating-point-variability-of-AI\/10.1117\/12.2612405.full"}},"subtitle":[],"editor":[{"given":"Claudia R.","family":"Mello-Thoms","sequence":"additional","affiliation":[]},{"given":"Sian","family":"Taylor-Phillips","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,4,4]]},"references-count":16,"URL":"https:\/\/doi.org\/10.1117\/12.2612405","relation":{},"subject":[],"published":{"date-parts":[[2022,4,4]]}}}