Lab of Medical Imaging and Computation

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…

★2024 Postdoctoral Scholar Position Available

If you are interested in applying for the position, please send your detailed CV along with a cover letter to the following email address: sdo@mgh.harvard.edu. We look forward to reviewing your application.

Nature Scientific Report

https://www.nature.com/articles/s41598-019-51779-5

“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,…

Intracranial Hemorrhage (ICH) Detection @ AuntMinnie

AuntMinnie reports (Dec 17, 2018, Ridley) “Researchers have developed a new artificial intelligence (AI) algorithm designed to address two of the biggest challenges in imaging AI: its “black box” nature and the need for large amounts of image data to train the models, according to a study published online December 17 in Nature Biomedical Engineering.”

“An Explainable Deep-learning Algorithm for the Detection of Acute Intracranial Haemorrhage from Small Datasets” Published in Nature Biomedical Engineering

“An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets (Hyunkwang Lee, Sehyo Yune, Mohammad Mansouri, Myeongchan Kim, Shahein H. Tajmir, Claude E. Guerrier, Sarah A. Ebert, Stuart R. Pomerantz, Javier M. Romero, Shahmir Kamalian, Ramon G. Gonzalez, Michael H. Lev & Synho Do)“ is published in Nature Biomedical Engineering. The article is available here.

“Practical Window Setting Optimization for Medical Image Deep Learning” Accepted by ML4H Workshop at NeurIPS 2018

“Practical Window Setting Optimization for Medical Image Deep Learning (Hyunkwang Lee, Myeongchan Kim, Synho Do)” is accepted by Machine Learning for Health (ML4H) Workshop at NeurIPS 2018. (Link) The article is available here.

“Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model” Published in Journal of Digital Imaging

“Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model (Sehyo Yune, Hyunkwang Lee, Myeongchan Kim, Shahein H. Tajmir, Michael S. Gee, Synho Do)“ is published in Journal of Digital Imaging. The article is available here.

3D Convolutional Neural Network for Voxel-level Prediction of Radiation Absorption (CMIMI 2018 Presentation)

Myeongchan Kim (MD) gave a presentation on his paper “3D Convolutional Neural Network for Voxel-level Prediction of Radiation Absorption” at 2018 SIIM Conference on Machine Intelligence in Medical Imaging (CMIMI). Kim, M., Li, X., Yune, S., Lee, H., Liu, B., Do, S., 2018. 3D Convolutional Neural Network for Voxel-level Prediction of Radiation Absorption. The risk of diagnostic imaging procedures such…