) 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.

Public Health Relevance

The UCLA Medical Imaging Informatics (MII) training program aims to train and inspire a new generation of imaging informaticians who will lead the development of innovative approaches that inform and enable precision medicine. MII establishes a cross-campus, interdisciplinary environment to teach and mentor future scientists in cutting-edge computational and data science methods towards imaging; and creates novel, team science re- search opportunities in which graduate students work and learn from leading experts in the field. MII trainees are exposed to the breadth of contemporary imaging informatics research, and are ultimately prepared to be productive, independent scientists that will participate in shaping the discipline as biomedical research and healthcare evolve.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Institutional National Research Service Award (T32)
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Special Emphasis Panel (ZEB1)
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Erim, Zeynep
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University of California Los Angeles
Schools of Medicine
Los Angeles
United States
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Caufield, John Harry; Liem, David A; Garlid, Anders O et al. (2018) A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts. J Vis Exp :
Garcia-Gathright, Jean I; Matiasz, Nicholas J; Adame, Carlos et al. (2018) Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies. Comput Biol Med 92:55-63
Garlid, Anders Olav; Polson, Jennifer S; Garlid, Keith D et al. (2017) Equipping Physiologists with an Informatics Tool Chest: Toward an Integerated Mitochondrial Phenome. Handb Exp Pharmacol 240:377-401
Shen, Shiwen; Han, Simon X; Petousis, Panayiotis et al. (2017) A Bayesian model for estimating multi-state disease progression. Comput Biol Med 81:111-120
Petousis, Panayiotis; Han, Simon X; Aberle, Denise et al. (2016) Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network. Artif Intell Med 72:42-55
Garcia-Gathright, Jean I; Matiasz, Nicholas J; Garon, Edward B et al. (2016) Toward patient-tailored summarization of lung cancer literature. IEEE EMBS Int Conf Biomed Health Inform 2016:449-452
Speier, W; Arnold, C W; Deshpande, A et al. (2015) Incorporating advanced language models into the P300 speller using particle filtering. J Neural Eng 12:046018
Speier, William; Deshpande, Aniket; Pouratian, Nader (2015) A method for optimizing EEG electrode number and configuration for signal acquisition in P300 speller systems. Clin Neurophysiol 126:1171-1177
McNamara, Mary; Sarma, Karthik; Aberle, Denise R et al. (2014) Data model for personalized patient health guidelines: an exploratory study. AMIA Annu Symp Proc 2014:1835-44