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Introduction

机译：介绍

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The 2019 SPIE "Biomedical Applications in Molecular, Structural and FunctionalImaging" Conference was held on 19 - 21 February, 2019 at Town and CountryResort, San Diego, California, United States. We maintained the high participationwe saw last year, both in the number of abstracts submitted and in the conferenceattendees over previous years. Conference Chairs Barjor Gimi and Andrzej Krolwelcomed Professor Christopher Filippi, North Shore-Long Island Jewish MedicalSystem and Columbia University (USA), who delivered an outstanding, insightfulkeynote address entitled "The dawning of AI in radiology: a brave new world" thatprovided a brief review of machine learning and deep learning techniques inartificial intelligence (AI), as applied in diagnostic radiology, with the focus on thetranslation of AI into diagnostic radiology from clinical workflow to itsimplementation in routine clinical practice to informed diagnosis, treatmentmanagement, and prognostication. Specific, ongoing work in the automateddetection of hemorrhage on non-contrast head CT, prediction of geneticvariability of brain tumors, detection of breast cancer and risk factors for breastcancer, and detection of knee ligament injury was profiled among other clinicalapplications. Both machine learning and deep learning techniques aretransforming how radiologists make intelligent decisions from the quantitativediagnostic images that will require that radiologists become data scientists anddata managers in the future.
机译：2019年的SPIE“分子，结构和功能的生物医学应用成像“会议于2019年2月19日至21日在镇和国家举行度假村，圣地亚哥，加利福尼亚州，美国。我们保持高度参与我们去年看到，两者都在提交和会议中的摘要数量去年的与会者。会议椅Barjor Gimi和Andrzej Krol欢迎克里斯托弗菲林迪教授，北岸长岛犹太医疗系统和哥伦比亚大学（美国），谁交付了一个杰出的洞察力主题演讲题为“放射学中AI的曙光：勇敢的新世界”提供了对机器学习和深度学习技术的简要介绍人工智能（AI），应用于诊断放射学，重点是ai将ai翻译成诊断放射学从临床工作流向其在常规临床实践中实施对知情诊断，治疗管理和预测。具体，正在进行的自动化工作检测对非对比头CT的出血，遗传学预测脑肿瘤的变异性，乳腺癌检测和乳腺危险因素癌症，以及膝关节损伤的检测是在其他临床上进行突变应用程序。两种机器学习和深度学习技术都是改变放射科医生如何从定量中做出智能决策诊断图像将要求放射科医生成为数据科学家和数据经理将来。

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