MIT researchers combine deep learning and physics to fix motion-corrupted MRI scans
The challenge involves more than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
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The challenge involves more than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
MIT researchers investigate the causes of health care disparities among underrepresented groups.
Each semester, students help Boston-area organizations with technical needs — pro bono.
Three graduate students forged a path to the same Picower Institute lab through participating in the MIT Summer Research Program in Biology and Neuroscience.
Developed by MIT researchers, BrightMarkers are invisible fluorescent tags embedded in physical objects to enhance motion tracking, virtual reality, and object detection.
The former director of LIDS was a beloved professor who blended intellectual rigor with curiosity.
With a new, user-friendly interface, researchers can quickly design many cellular metamaterial structures that have unique mechanical properties.
“PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.
Faculty and researchers across MIT’s School of Engineering receive many awards in recognition of their scholarship, service, and overall excellence.
The founders of MIT spinout Active Surfaces describe their thin-film solar technology and their experience winning this year’s $100K.
The device detects the same molecules that cell receptors do, and may enable routine early screening for cancers and other diseases.
A new technique helps a nontechnical user understand why a robot failed, and then fine-tune it with minimal effort to perform a task effectively.
EECS professor appointed to new professorship in the MIT Schwarzman College of Computing.
PIGINet leverages machine learning to streamline and enhance household robots' task and motion planning, by assessing and filtering feasible solutions in complex environments.
Researchers create a privacy technique that protects sensitive data while maintaining a machine-learning model’s performance.