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Teaching AI to Spot Brain Damage in Real-World MRI Scans

Clinical brain scans vary widely, making automated detection of white matter damage unreliable. Using real-world MRI data, we built and tested new AI models that learn to handle this variability, enabling more accurate and clinically useful brain lesion detection.
Go to the profile of Junjie Wu
Junjie Wu
Researcher, Emory University
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Benchmark White Matter Hyperintensity Segmentation Methods Fail on Heterogeneous Clinical MRI: A New Dataset and Deep Learning–Based Solutions

Brain scans from hospitals don’t all look the same—they come from different machines, settings, and years. This makes it hard for AI tools to reliably detect small areas of brain damage linked to aging and disease. In this work, we used real clinical MRI scans to train smarter AI models that can adapt to this variability, bringing automated brain analysis closer to everyday medical use.

Go to the profile of Junjie Wu
Junjie Wu
Researcher, Emory University

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Follow the Topic

Alzheimer's disease
Life Sciences > Health Sciences > Clinical Medicine > Neurology > Neurological Disorders > Neurodegenerative diseases > Alzheimer's disease
Magnetic Resonance Imaging
Life Sciences > Health Sciences > Radiology > Nuclear Medicine > Magnetic Resonance Imaging
Medical Imaging
Technology and Engineering > Biological and Physical Engineering > Biomedical Engineering and Bioengineering > Biomedical Devices and Instrumentation > Medical Imaging
Neuroradiology
Life Sciences > Health Sciences > Radiology > Neuroradiology
White matter injury
Life Sciences > Health Sciences > Clinical Medicine > Neurology > Neurological Disorders > Brain Injuries > White matter injury
Stroke
Life Sciences > Health Sciences > Clinical Medicine > Diseases > Cardiovascular Diseases > Vascular Diseases > Cerebrovascular Disorders > Stroke

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