Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI. N Lassau , T Estienne , P de Vomecourt , M Azoulay , J Cagnol , G Garcia , M Majer , E Jehanno , R Renard-Penna , C Balleyguier , F Bidault , C Caramella , T Jacques , F Dubrulle , J Behr , N Poussange , J Bocquet , S Montagne , F Cornelis , M Faruch , B Bresson , S Brunelle , A Jalaguier-Coudray , N Amoretti , A Blum , A Paisant , V Herreros , O Rouviere , S Si-Mohamed , L Di Marco , O Hauger , M Garetier , F Pigneur , A Bergère , C Cyteval , L Fournier , C Malhaire , J-L Drape , E Poncelet , C Bordonne , H Cauliez , J-F Budzik , M Boisserie , T Willaume , S Molière , N Peyron Faure , S Caius Giurca , V Juhan , T Caramella , A Perrey , F Desmots , M Faivre-Pierre , M Abitbol , R Lotte , D Istrati , D Guenoun , A Luciani , M Zins , J-F Meder , A Cotten Diagnostic and Interventional Imaging（2019）
摘要
Theses five challenges were able to gather a large community of radiologists, engineers, researchers, and companies in a very short period of time. The accurate results of three of the five modalities suggest that artificial intelligence is a promising tool in these radiology modalities.
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关键词
Artificial intelligence (AI), Deep learning, Ultrasound, Magnetic resonance imaging (MRI), Computed Tomography (CT)
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