Nature Biomedical Engineering

Tackling prediction uncertainty in machine learning for healthcare The article emphasizes the need for prediction-uncertainty metrics in healthcare applications, particularly radiology. It discusses the implementation of these metrics in error-intolerant and error-tolerant applications. It provides a framework for understanding prediction uncertainty in healthcare. It highlights the need for machine-learning models with zero tolerance for false-positive…

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2023-2024 Postdoctoral Scholar Position Available


Nature Scientific Report

“Five” Oral Presentations Accepted at C-MIMI 2019

  GrayNet: A Versatile Base Model for Practical Deep Learning CT ApplicationsMyeongchan Kim, MD, Massachusetts General Hospital, Hyunkwang Lee, MS; Kyungdoo Song, MD; Sehyo Yune, MD, MPH, MBA; Poornima Ramaraj, MS; Choonsik Lee, PhD; Jinserk Baik, PhD; Synho Do, PhD Distributed Single-model Training in Inter-institutional Collaboration without Exposing Each DatasetJinserk Baik, PhD, Massachusetts General HospitalMyeongchan Kim,…

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Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis Paper published titled, “Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis” Lee, H., Troschel, F.M., Tajmir, S. et al. J Digit Imaging (2017). doi:10.1007/s10278-017-9988-z. Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an “eyeball test” to assess whether patients will tolerate major surgery or…

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Patient-specific Radiation Dose Estimates@AuntMinnie AI can yield patient-specific radiation dose estimates By Erik L. Ridley, AuntMinnie staff writer Ridley reports,  “A machine-learning algorithm shows potential for facilitating the holy grail of real-time, patient-specific radiation dose estimates from CT scans, according to research presented at the Society for Imaging Informatics in Medicine (SIIM) annual meeting”.  “In a proof-of-concept study,…

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PICC Line Detection @ AuntMinnie Deep-Learning Algorithm May Be Able To Detect An Incorrectly Positioned Peripherally Inserted Central Catheter, Study Suggests. Aunt Minnie (6/12, Ridley) reports “a deep-learning algorithm that can prescreen chest radiographs for an incorrectly positioned peripherally inserted central catheter (PICC) was presented at the recent Society for Imaging Informatics in Medicine (SIIM) annual meeting.” Researchers “developed…

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Positions Available

There are currently positions available for interns, PhD students, and Postdoctoral Fellows in the Laboratory of Medical Imaging and Computation. We invite outstanding individuals to join the vibrant collaborative research setting by working closely with MGH clinicians, researchers, and engineers. Please apply by sending a resume or CV to Dr. Synho Do at

“Machine Intelligence for Accurate X-ray Screening and Read-out Prioritization: PICC line Detection Study” : Accepted for presentation at the 2017 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM)

 Authors: Hyunkwang Lee, Jordan Rogers, Junghwan Cho, Dania Daye, Vishala Mishra, Garry Choy, Shahein Tajmir, Michael Lev and Synho Do Congratulations! Your abstract has been accepted for presentation at the 2017 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM). The meeting will be held Thursday, June 1 – Saturday, June 3, 2017…

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