“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.
The software tool NeuroTrALE is designed to quickly and efficiently process large amounts of brain imaging data semi-automatically.
The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
By providing plausible label maps for one medical image, the Tyche machine-learning model could help clinicians and researchers capture crucial information.
MIT researchers plan to search for proteins that could be used to measure electrical activity in the brain.
Using a machine-learning algorithm, researchers can predict interactions that could interfere with a drug’s effectiveness.
Study shows computational models trained to perform auditory tasks display an internal organization similar to that of the human auditory cortex.
A new method enables optical devices that more closely match their design specifications, boosting accuracy and efficiency.
By analyzing bacterial data, researchers have discovered thousands of rare new CRISPR systems that have a range of functions and could enable gene editing, diagnostics, and more.
Two studies find “self-supervised” models, which learn about their environment from unlabeled data, can show activity patterns similar to those of the mammalian brain.
