2023 Summer Intern Research

For the LMIC 2023 summer research internship, “Justin Hwang” has provided a summary of his research, final report, and corresponding GitHub repository.   https://github.com/justinhwang24/lung-abnormality-detection   Summary: Machine learning aids in diagnosing lung abnormalities from medical images. This study used convolutional neural networks (CNNs) to detect these abnormalities in 1,740 chest radiographs, achieving 93.10% accuracy. Such…

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Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model

Doyun Kim,  Joowon Chung,  Jongmun Choi,  Marc D. Succi,  John Conklin,  Maria Gabriela Figueiro Longo,  Jeanne B. Ackman,  Brent P. Little,  Milena Petranovic,  Mannudeep K. Kalra,  Michael H. Lev &  Synho Do

Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach

Joowon Chung,  Doyun Kim,  Jongmun Choi,  Sehyo Yune,  Kyoung Doo Song,  Seonkyoung Kim,  Michelle Chua,  Marc D. Succi,  John Conklin,  Maria G. Figueiro Longo,  Jeanne B. Ackman,  Milena Petranovic,  Michael H. Lev &  Synho Do 

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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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.

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