Our mission
_____________
Our mission in the HITI lab is to apply machine learning to big problems in healthcare, including developing diagnostic and predictive imaging models, validation of existing models, and a focus on the fairness and explainability of AI.
Our projects encompass a wide range of topics from computer vision to natural language processing to radiology workflow. We strive to base each project on a real clinical need and end with the implementation of our products into a clinical workflow.
Latest News
New publication in JAMA
A large, multiinstutional collaborative paper “Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms” was published in JAMA. This work evaluates several deep learning models’ performance on detecting cancer in...
New Collaboration with Stanford University and UCSF for HCC
We are pleaseed to announch a new collaboration with Stanford University and University of California, San Francisco to automatically infer risk score for hepatocellular carcinoma (HCC) from MRI images.
New publication in npj Digital Medicine
Our paper “Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines“ provides an overview of different types of fusion models that can merge clinical and imaging data to imrpove diagnostic and...
New publication in JACR
Our paper ““Current clinical applications of #AI in radiology and their best supporting evidence “ was just published in the Journal of the American College of Radiology
New Collaboration with Kheiron Medical for Breast Cancer Screening
The HITI lab is happy to announce a collaboration with U.K. based Kheiron Medical to test and improve performance of their breast cancer detection model – Mia. Our diverse patient population will allow us to ensure that these types of models serve all populations...
ACR DSI Lab Pilot Site
The HITI lab is proud to be one of the 7 pilot sites for the American College of Radiology AI Lab to work on federated learning. This work will improve robustness of machine learning models by allowing training across multiple institutions to improve generalizability
Projects
_____________
ACR DSI Lab pilot site
Machine learning for inferencing of Hepatocellular Carcinoma (HCC) risk score from medical imaging
AI model development for breast cancer treatment and outcome management
Mammography dataset creation
Recent Publications
_____________
Sorry, no publications matched your criteria.
Team
_____________
Hari Trivedi, Faculty
Assistant Professor, Department of Radiology
Imon Banerjee, Faculty
Assistant Professor, Department of Biomedical Informatics
Judy Gichoya, Faculty
Assistant Professor, Department of Radiology