ABOUT CAMCA
The MGH/HMS Center for Advanced Medical Computing and Analysis gathered a group of passionate experts in data science, machine learning, artificial intelligence and medical image analysis. Our vision is to build smart healthcare delivery frameworks by bridging the expertise from clinical experts into the most cutting-edge AI systems. Currently we are exploring the application of AI in various medical fields including detection, diagnosis, prognosis and radiation oncology.
HIGHLIGHTED RESEARCH
Large Language Model for Healthcare
We have conducted a wide range of research on developing, improving, and harnessing Large language model (LLM) solutions for healthcare applications.
Diffusion Model
We have conducted extensive research on the topic of diffusion models in both algorithm development and application in healthcare.
Foundation Model in Medical Image Analysis
We have undertaken broad research in the development of medical imaging foundation models and the adaptation of general visual foundation models to medical applications.
LATEST POSTS
- CAMCA’s Latest Research Paper “Motion Correction and Super-Resolution for Multi-slice Cardiac Magnetic Resonance Imaging via an End-to-End Deep Learning Approach”Are you encountering challenges when using 2D Cardiac MR slices for 3D Cardiac analysis? Here are two major hurdles: 1. Slice thickness (usually 8-10mm) results in missing data between neighboring slices.2. Misalignment between slices due to motion. These issues impact the fidelity of 2D CMR
- CAMCA’s Xiang Li and Quanzheng Li Recipient for Google Research Scholar Program!Thrilled to share that Xiang Li and Quanzheng Li have been selected as a recipient for the prestigious Google Research Scholar Program! This recognition highlights our commitment to advancing healthcare through innovative research. Grateful for the opportunity and excited to make meaningful contributions to the
- CAMCA Paper Published in the American Association of Physicists in MedicineOur latest study “Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning” led by Zhennong Chen and Dufan Wu takes on the challenge of imperfections in head motion artifacts in head CT! With combined power of “partial-angle