Researchers have shown that federated learning is successful in the context of brain imaging, by being able to analyze MRI scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.
Pathologists who examined the computationally stained images could not tell them apart from traditionally stained slides.
Researchers have developed a new approach to early diagnosis of lung cancer: a urine test that can detect the presence of proteins linked to the disease.
Researchers demonstrated a methodology that combines the bioprinting and imaging of glioblastoma cells in a way that more closely models what happens inside the human body.
Using a robot to treat brain aneurysms is feasible and could allow for improved precision when placing stents, coils and other devices.
A novel method of combining advanced optical imaging with an artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of brain tumors.
A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.
An AI platform can analyze genomic data extremely quickly, picking out key patterns to classify different types of colorectal tumors and improve the drug discovery process.
Researchers used artificial intelligence to develop a new classification method which identifies the primary origins of cancerous tissue based on chemical DNA changes.
Researchers have built a set of magnetic ‘tweezers’ that can position a nano-scale bead inside a human cell in three dimensions with unprecedented precision.
Thanks to developments in 3D bioprinting, the UT researchers could create a miniature brain model representing the delicate tissue around the tumor, including the macrophages.