Medical Imaging Informatics Training Grant

摘要(项目描述) 随着生物医学数字时代的到来，令人兴奋的机会出现了，以革命性的方式执行科学- 专门的研究和提供医疗保健。精准医学等新兴领域预示着 我们如何理解疾病及其个性化的治疗。在此背景下，成像的重要性 继续增长，既是新知识的驱动力，也是利用这些洞察力更好地 为患者进行检测、诊断和治疗。但要实现这一未来的全部承诺，仍然需要-- 随之而来的是许多障碍，新的成像信息学家必须配备尖端技能， 创建和支持必要的计算进步和方法。 加州大学洛杉矶分校医学影像信息学(MII)培训计划的目标是成为培训下一代的领导者 他们将开发所需的计算方法和应用程序，以实现 这个未来。汇聚了来自我们机构的成像、工程(计算机和 数据科学、电气、生物工程)、(生物)统计学和医学，信息产业部设想了一个环境促进 跨学科教学和对学生的指导；促进整个领域的创新研究 影像信息学。信息产业部的培训计划包括一个为期一年的全面核心课程，介绍基金- 学科的教学原则，形成了理解的广度，同时加强了技术水平-- 任何成像信息学家都需要CII。学生完成课程作业，涵盖 医学成像和医疗保健的前景，包括：信息架构；数据和知识表示 数据挖掘；机器学习；生物统计学；信息检索。交叉专题(例如，辐射-- 基因组学、多模式数据集成和生物标记物开发)贯穿于这些课程。 与核心课程并行，学生立即从事研究，完成与教员的轮换 以获得对当代成像信息学项目的欣赏。有了这样的经历，博士生们分成了 随后，通过更高级的选修课程，根据他们特定的研究兴趣进行专业学习。 学生将面临提出、开发和测试新的成像信息学方法的挑战，这些方法将推动 纪律，以及最终改变和影响医疗保健。重要的是，培训和研究都是相互关联的 在生物医学应用领域内编织，并拥有适当的博士和MD导师，以确保复合- 分析/信息学、临床和真实世界的翻译见解和指导。认识到不断演变的土地- 我们的T32包括许多专业发展活动，包括 实习，在不同的环境中提供实践(研究)经验。通过在此过程中获得的经验- 在这个T32培训计划中，信息产业部的学生将成为独立的科学家，准备为 引领影像信息学的发展。

ABSTRACT (PROJECT DESCRIPTION) With the “digital era of biomedicine” upon us, exciting opportunities arise to revolutionize how we perform scien- tific research and deliver healthcare. Burgeoning areas like precision medicine foreshadow a transformation of how we understand disease and its individually-tailored treatment. In this context, the importance of imaging continues to grow, both as a driver of new knowledge and as a vital tool which uses these insights towards better detection, diagnosis, and treatment for patients. But achieving the full promise of this future still requires over- coming many barriers, and new imaging informaticians must be equipped with the cutting-edge skills that will create and support the necessary computational advances and methods. The UCLA Medical Imaging Informatics (MII) training program aims to be a leader in training this next generation of imaging informaticians who will develop the needed computational approaches and applications that enable this future. Bringing together leading experts from across our institution in imaging, engineering (computer and data science, electrical, bioengineering), (bio)statistics, and medicine, MII envisions an environment fostering interdisciplinary teaching and mentoring of students; and promoting innovative research throughout the spectrum of imaging informatics. MII's training program involves a comprehensive 1-year core curriculum introducing foun- dational principles of the discipline, forming a breadth of understanding while reinforcing the technical proficien- cies needed by any imaging informatician. Students complete coursework covering topics presented from the perspective of medical imaging and healthcare, including: information architectures; data and knowledge repre- sentation; data mining; machine learning; biostatistics; and information retrieval. Cross-cutting topics (e.g., radi- ogenomics, multimodal data integration and biomarker development) are presented throughout these courses. In parallel to the core curriculum, students are immediately engaged in research, completing rotations with faculty to gain an appreciation for contemporary imaging informatics projects. With this experience, PhD students sub- sequently specialize via more advanced elective coursework customized to their particular research interests. Students are challenged to propose, develop, and test new imaging informatics methods that will advance the discipline, as well as ultimately change and affect healthcare. Importantly, both training and research are inter- woven within a biomedical application domain and with appropriate PhD and MD mentorship to ensure compu- tational/informatics, clinical, and real-world translational insights and guidance. Recognizing the evolving land- scape of the biomedical workforce, our T32 includes a number of professional development activities, including internships, providing practical (research) experiences in different settings. Through the experiences gained dur- ing this T32 training program, MII students will become independent scientists, prepared to contribute to and lead imaging informatics as it continues to grow.