Lab of Medical Imaging and Computation

Patient-specific Radiation Dose Estimates@AuntMinnie

http://www.auntminnie.com/index.aspx?sec=sup&sub=cto&pag=dis&ItemID=117679 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,…

PICC Line Detection @ AuntMinnie

http://www.auntminnie.com/index.aspx?sec=sup&sub=aic&pag=dis&ItemID=117547 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…

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 sdo@mgh.harvard.edu

“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…

“Machine Learning Powered Automatic Organ Classification for Patient Specific Organ Dose Estimation” Accepted for presentation at the 2017 Annual Meeting of the Society of Imaging Informatics in Medicine (SIIM)

Authors: Junghwan Cho, Eunmi Lee, Hyunkwang Lee, Bob Liu, Xinhua Li, Shahein Tajmir, Dushyant Sahani, and Synho Do The 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 at the David…

Committee Member on Machine Intelligence, Society for Imaging Informatics in Medicine (SIIM)

Selected as a committee member on Machine Intelligence, Society for Imaging Informatics in Medicine at SIIM.

2017 GPU Technology Conference (GTC) : Presentation Accepted

GTC takes place May 8-11 at the San Jose McEnery Convention Center in San Jose, California. http://www.gputechconf.com/ Learn about state-of-art and practical medical image machine learning projects, which will be tested in hospitals. Presently, high performance computing systems are the most crucial components of the machine learning system. They are relatively inexpensive and very efficient…

Fully Automated Deep Learning System for Bone Age Assessment

Journal of Digital Imaging (JDI-16-10-0276.R1) Authors: Hyunkwang Lee, Shahein Tajmir, Jenny Lee, Maurice Zissen, Bethel Ayele Yeshiwas,Tarik K. Alkasab, Garry Choy, and Synho Do

Invited talk: Integrative Biomedical Imaging Informatics at Stanford (IBIIS)

http://ibiis.stanford.edu/events/seminars/2017seminarseries.html

Invited Talk: GPU Technology Conference (Washington DC, 2016)

Title: Medical Image Deep Learning with a Supercharged Machine Learning System DGX-1 Presented by NVIDIA, the GPU Technology Conference (GTC) is the world’s most important event series for GPU developers. Experience two days of hands-on AI training, industry insights, and direct access to NVIDIA experts-all in one place. Secure your spot now. http://dc.gputechconf.com/ Healthcare track: Join…