Data Engineering for Machine Learning in Women's Imaging and Beyond.
Chen Cui,Shinn-Huey S. Chou,Laura J. Brattain,Laura J. Brattain,Constance D. Lehman,Anthony E. Samir +5 more
TLDR
The focus of this article is women's imaging; nonetheless, the principles described apply to all domains of medical imaging, including databases, data integrity, and characteristics of data suitable for machine learning projects.Abstract:
OBJECTIVE. Data engineering is the foundation of effective machine learning model development and research. The accuracy and clinical utility of machine learning models fundamentally depend on the ...read more
Citations
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Artificial intelligence and women's health.
TL;DR: It was identified that there are several methods in the use of AI to help the screening of groups at risk for osteoporosis or fractures, and Artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women have been investigated.
Journal ArticleDOI
Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review
TL;DR: A panoramic view of how AI is poised to enhance breast imaging procedures is provided in this paper , where the authors capture the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review.
Book ChapterDOI
Artificial Intelligence for the Comprehensive Development of Household Makers (Especially in South Asian Countries)
TL;DR: In this article , a group survey is taken in India, Sri Lanka, Singapore, Malaysia, Thailand, Myanmar, and Nepal covering around 200 women about their basic problems in personal and professional lives and lifestyle and house management routine activities.
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Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.
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The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.
Samuel G. Armato,Geoffrey McLennan,Luc Bidaut,Michael F. McNitt-Gray,Charles R. Meyer,Anthony P. Reeves,Binsheng Zhao,Denise R. Aberle,Claudia I. Henschke,Eric A. Hoffman,Ella A. Kazerooni,Heber MacMahon,Edwin J. R. van Beek,David F. Yankelevitz,Alberto Biancardi,Peyton H. Bland,Matthew S. Brown,Roger Engelmann,Gary E. Laderach,Daniel Max,Richard C. Pais,David Qing,Rachael Y. Roberts,Amanda R. Smith,Adam Starkey,Poonam Batra,Philip Caligiuri,Ali Farooqi,Gregory W. Gladish,C. Matilda Jude,Reginald F. Munden,Iva Petkovska,Leslie E. Quint,Lawrence H. Schwartz,Baskaran Sundaram,Lori E. Dodd,Charles Fenimore,David Gur,Nicholas Petrick,John Freymann,Justin Kirby,Brian Hughes,Alessi Vande Casteele,Sangeeta Gupte,Maha Sallam,Michael D. Heath,Michael Kuhn,Ekta Dharaiya,Richard Burns,David Fryd,Marcos Salganicoff,Vikram Anand,Uri Shreter,Stephen Vastagh,Barbara Y. Croft,Laurence P. Clarke +55 more
TL;DR: The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus and is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.
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Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.
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Breast cancer statistics, 2017, racial disparity in mortality by state.
TL;DR: An overview of female breast cancer statistics in the United States, including data on incidence, mortality, survival, and screening, suggests improving access to care for all populations could eliminate the racial disparity in breast cancer mortality and accelerate the reduction in deaths nationwide.