Generative AI improves a wireless vision system that sees through obstructions
With this new technique, a robot could more accurately detect hidden objects or understand an indoor scene using reflected Wi-Fi signals.
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Imaging
AI algorithm enables tracking of vital white matter pathways
Opening a new window on the brainstem, a new tool reliably and finely resolves distinct nerve bundles in live diffusion MRI scans, revealing signs of injury or disease.
Read MoreSMART launches new Wearable Imaging for Transforming Elderly Care research group
WITEC is working to develop the first wearable ultrasound imaging system to monitor chronic conditions in real-time, with the goal of enabling earlier detection and timely intervention.
Read MoreA “scientific sandbox” lets researchers explore the evolution of vision systems
The AI-powered tool could inform the design of better sensors and cameras for robots or autonomous vehicles.
Read MoreDeep-learning model predicts how fruit flies form, cell by cell
The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.
Read MoreChecking the quality of materials just got easier with a new AI tool
Acting as a “virtual spectrometer,” SpectroGen generates spectroscopic data in any modality, such as X-ray or infrared, to quickly assess a material’s quality.
Read MoreNew AI system could accelerate clinical research
By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.
Read MoreMachine-learning tool gives doctors a more detailed 3D picture of fetal health
MIT CSAIL researchers developed a tool that can model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses.
Read MoreMaking AI models more trustworthy for high-stakes settings
A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.
Read MoreThe framework helps clinicians choose phrases that more accurately reflect the likelihood that certain conditions are present in X-rays.
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