QTIM @ MGH
The Quantitative Translational Imaging in Medicine Lab at the Martinos Center
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Our lab focuses on developing quantitative imaging biomarkers for cancer and other diseases using advanced imaging techniques and machine learning methods. We are comprised of computer science researchers, medical physicists, neuro-oncologists, and MRI technicians, and we are always looking to collaborate with experts outside of our field. We have recently worked to apply deep learning methods to a variety of diseases, and our goal is to unite the cutting edges of machine learning, medical oncology, and image analysis into practical clinical applications.
Read about our lab members on the People tab. Learn more about our specific research topics on the Research tab. See our recent publications on the Publications tab, job openings on the Jobs tab, and find a way to get in touch on the Contact tab. Last but not least, check the Fun tab to see some pictures of our lab members doing what we do best.
- 7/22/2020 - QTIM releases Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs using Convolutional Siamese Neural Networks! We developed an automated Siamese neural network-based pulmonary disease severity score for patients with COVID-19, with the potential to help with clinical triage and workflow optimization.
- 6/24/2020 - QTIM releases breast density classification algorithm in JACR! This article demonstrates the possible parameters that can influence the performance of the model and how crowdsourcing can be used for evaluation.
- 6/23/2020 - DeepNeuro: An Open-Source Deep Learning Toolbox for Neuroimaging was published in PebMed. DeepNeuro is a Python-based deep learning framework that puts deep neural networks for neuroimaging into practical usage with a minimum of friction during implementation.
- 5/15/2020 - QTIM's paper Assessing the validity of saliency maps for abnormality localization in medical imaging was published as part of MIDL Montréal 2020, a conference which brings deep learning and medical imaging researchers together for in-depth discussion and exchange of ideas.
- 3/10/2020 - Katharina Hoebel's paper An exploration of uncertainty information for segmentation quality assessment was published as part of SPIE medical imaging proceedings.
- 3/6/2020 - Siamese neural networks for evaluation of disease severity and change on a continuous spectrum in medical imaging accepted to npj Digital Medicine
- 11/13/2019 - Sean Ko's abstract Machine Learning Based Predictive Model of 5-Year Survival in Multiple Myeloma Autologous Transplant Patients is published in The American Society of Hematology
- 10/18/2019 - Post Doc Ikbeom Jang joined the lab
- 10/18/2019 - Masters student Bryan Chen joined the lab
- 10/17/2019 - Newly Published Literature: QTIM paper An exploration of uncertainty information for segmentation quality assessment has been accepted for oral presentation at SPIE Medical Imaging 2020!
- 10/07/2019 - Masters student Sean Ko joined the lab
- 10/01/2019 - Katharina Höbel's extended abstract Give me (un)certainty - An exploration of parameters that affect segmentation uncertainty was accepted to ML4H at NeurIPS 2019
- 9/26/2019 - Newly Published Literature: Bevacizumab reduces permeability and concurrent temozolomide delivery in a subset of patients with recurrent glioblastoma.
- 7/15/2019 - Data Scientist Ikbeom Jang joined the lab
- 7/9/2019 - Newly Published Literature: Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture.
- 7/2019 - Newly Published Literature: Democratizing AI.
- 6/13/2019 - Postdoc Praveer Singh joined the lab