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How will baseflow respond to climate change in the Upper Colorado River Basin? 期刊论文
Geophysical Research Letters, 2021
作者:  Olivia L. Miller;  Matthew P. Miller;  Patrick C. Longley;  Jay R. Alder;  Lindsay A. Bearup;  Tom Pruitt;  Daniel K. Jones;  Annie L. Putman;  Christine A. Rumsey;  Tim McKinney
收藏  |  浏览/下载:22/0  |  提交时间:2021/11/15
International evaluation of an AI system for breast cancer screening 期刊论文
NATURE, 2020, 577 (7788) : 89-+
作者:  McKinney, Scott Mayer;  Sieniek, Marcin;  Godbole, Varun;  Godwin, Jonathan;  Antropova, Natasha;  Ashrafian, Hutan;  Back, Trevor;  Chesus, Mary;  Corrado, Greg C.;  Darzi, Ara;  Etemadi, Mozziyar;  Garcia-Vicente, Florencia;  Gilbert, Fiona J.;  Halling-Brown, Mark;  Hassabis, Demis;  Jansen, Sunny;  Karthikesalingam, Alan;  Kelly, Christopher J.;  King, Dominic;  Ledsam, Joseph R.;  Melnick, David;  Mostofi, Hormuz;  Peng, Lily;  Reicher, Joshua Jay;  Romera-Paredes, Bernardino;  Sidebottom, Richard;  Suleyman, Mustafa;  Tse, Daniel;  Young, Kenneth C.;  De Fauw, Jeffrey;  Shetty, Shravya
收藏  |  浏览/下载:69/0  |  提交时间:2020/07/03

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful(1). Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives(2). Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.


  
Deep biogenic methane and drilling-associated gas artifacts: Influence on gas-based characterization of petroleum fluids 期刊论文
AAPG BULLETIN, 2020, 104 (4) : 887-912
作者:  Strapoc, Dariusz;  Jacquet, Benjamin;  Torres, Oscar;  Khan, Shahnawaz;  Villegas, Esra Inan;  Albrecht, Heidi;  Okoh, Bruno;  McKinney, Daniel
收藏  |  浏览/下载:16/0  |  提交时间:2020/05/13