SIIM 2018 New Investigator Travel Award Recipient

Travel awards are presented to new investigators with an area of study in imaging informatics and who either are currently in a full-time training program or have completed their training program within 2 years of the SIIM Annual Meeting. Our team member Sehyo Yune, MD, MPH, MBA was awarded SIIM 2018 New Investigator Travel Award! Yune,…

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Hyunkwang Lee gave a presentation on his paper at MGH Graduate Student Lunch Seminar

Hyunkwang Lee, PhD Candidate in Electrical Engineering, Harvard School of Engineering and Applied Sciences, MGH Lab of Medical Imaging and Computation, gave a presentation on his paper “Machine Friendly Machine Learning: Deep Dive into Raw Data Domain for Medical Image Analysis”  at MGH Graduate Student Lunch Seminar on Tuesday, July 10, 2018. His paper was presented at…

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Patient- and Organ-Specific CT Radiation Dose Estimation Using AI-based Body Part Classifier : GrayNet and NCICT (SIIM 2018 Presentation)

Myeongchan Kim (MD) gave a presentation on his paper “Patient- and Organ-Specific CT Radiation Dose Estimation Using AI-based Body Part Classifier : GrayNet and NCICT” at SIIM 2018 Annual Meeting. Kim, M., Yune, S., Lee, H., Lee, C., Liu, B., Li, X., Do, S., 2018. Patient- and Organ-Specific CT Radiation Dose Estimation Using AI-based BodyPart Classifier:…

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What Machines Can Read: Gender Identification from Hand and Wrist Radiographs in Children (SIIM 2018 Presentation)

Sehyo Yune (MD, MPH, MBA) gave a presentation on her paper “What Machines Can Read: Gender Identification from Hand and Wrist Radiographs in Children” at SIIM 2018 Annual Meeting. Yune, S., Lee, H., Kim, M., Tajmir, S., Gee, M., Lev, M., Do, S., 2018. What Machines Can Read: Gender Identification from Hand and Wrist Radiographs in…

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Machine Friendly Machine Learning: Deep Dive into Raw Data Domain for Medical Image Analysis (SIIM 2018 Presentation)

Hyunkwang Lee (Ph.D. Candidate in Electrical Engineering, Harvard School of Engineering and Applied Sciences) gave a presentation on his paper “Machine Friendly Machine Learning: Deep Dive into Raw Data Domain for Medical Image Analysis” at SIIM 2018 Annual Meeting. Lee, H., Yune, S., Kim, M., Brady, T., Lev, M., Do, S., 2018. Machine Friendly Machine Learning: Deep…

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Interventional Radiology Training Using a Dynamic Medical Immersive Training Environment (DynaMITE)

Link to the article “Interventional Radiology Training Using a Dynamic Medical Immersive Training Environment (DynaMITE)” (Authors: Colin J. McCarthy, Alvin Y.C. Yu, Synho Do, Steven L. Dawson, Raul N. Uppot) was published on JACR, Journal of the American College of Radiology.

2018 Herchel Smith Fellowship Recipient

Herchel Smith fellowships aim to encourage Harvard graduates of high academic ability and research promise in the sciences to study at Cambridge University.  Fellowships are held at Emmanuel College.  Awardees are to undertake studies appropriate to preparation for a future Ph.D. degree in mathematics, natural sciences, physical sciences, or applied sciences. Our intern Alli Welton…

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To Become Leaders in AI, Radiologists Must Address a Variety of Challenges

Radiology Business, Feb 16, 2018, Michael Walter Artificial intelligence (AI) is one of the biggest topics in healthcare today, and the authors of a recent analysis published in the Journal of the American College of Radiology (“Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success” James H. Thrall, Xiang Li, Quanzheng Li, Cinthia Cruz, Synho Do, Keith…

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Four Paper Abstracts Accepted for Presentation at the 2018 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM)

“Patient- and Organ-Specific CT Radiation Dose Estimation Using AI-based Body Part Classifier : MGH 23 Body-Part and NCICT” Authors: Myeongchan Kim, Sehyo Yune, Hyunkwang Lee, Bob Liu, Xinhua Li, Choonsik Lee, Synho Do “What Machines Can Read: Gender Identification from Hand and Wrist Radiographs in Children” Authors: Sehyo Yune, Hyunkwang Lee, Myeongchan Kim, Shahein Tajmir, Michael Gee, Michael Lev,…

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