A team of researchers at Washington University School of Medicine have developed a deep learning model that is capable of classifying a brain tumor as one of six common types using a single 3D MRI scan.
Researchers have developed a new type of atomic sensor made of boron nitride. It is based on a qubit in the crystal lattice and is superior to comparable sensors.
Engineers and ophthalmologists have developed a robotic imaging tool that can automatically detect and scan a patient's eyes for markers of different eye diseases.
Researchers developed a wearable X-ray detector prepared from nontoxic metal-organic frameworks layered between flexible plastic and gold electrodes for high-sensitivity sensing and imaging.
Researchers have developed a rapid and cost-effective particle agglutination based sensor that is powered by holographic imaging and deep learning
Scientists at have shown that diagnostic nanoparticles could be used to monitor tumor recurrence after treatment or to perform routine cancer screenings.
Scientists have leveraged artificial intelligence to train computers to keep up with the massive amounts of X-ray data taken at the Advanced Photon Source.
AI tools models are a powerful tool in cancer treatment. However, unless these algorithms are properly calibrated, they can sometimes make inaccurate or biased predictions.
Researchers have developed a new artificial intelligence tool that is able to automatically measure the amount of fat around the heart from MRI scan images.
Research using machine learning on images of everyday items is improving the accuracy and speed of detecting respiratory diseases, reducing the need for specialist medical expertise.
A neuroscientist at University of Texas at Austin wants to democratize the field and support infrastructure.
Researchers have developed a holographic technique that can rapidly reconstruct microscopic images of samples with up to 50-fold acceleration compared to existing methods.
Researchers discovered that AI models have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings.
An artificial intelligence (AI) program accurately predicts the risk that lung nodules detected on screening CT will become cancerous, according to a new study.
Researchers have developed a new "multi-modal" image fusion method based on supervised deep learning that enhances image clarity, reduces redundant image features and supports batch processing.
Scientists have developed a new microscopic imaging approach to take a closer look at 3D printing for developing future patient implants, as well as improved disease modelling and drug screening.
Neural network framework may increase radiologist's confidence in assessing the type of lung cancer on CT scans, informing individualized treatment planning.
Researchers used nanophotonic technology to develop a brain-implantable tool that can aid in the optical imaging of brain activity.
Development of smartphone-based imaging systems for medicine and healthcare can be optimized by newly published guidelines for holistic assessment.
Scientists have designed a portable 3D imaging device which will improve the treatment and diagnosis of cancer.
A new class of quantum dots opens a range of practical applications, including medical imaging and diagnostics and quantum communication.
A new method called tensor holography could enable the creation of holograms for virtual reality, 3D printing, medical imaging, and more — and it can run on a smartphone.
Researchers have found that out of the more than 300 COVID-19 machine learning models are not suitable for detecting or diagnosing COVID-19 from standard medical imaging.
We spoke with Prof. Dominic Zerulla, whose company PEARlabs is developing an imaging technique that sets out to push the boundaries once more – by looking at in-vivo nano-scale processes in motion.
A researcher has demonstrated a technique that reduces the computing time for non-rigid point set registration relative to other approaches.
A machine learning algorithm helps accurately differentiate benign and premalignant colorectal polyps on CT colonography scans.
Researchers at the Indian Institute of Science and SigTuple Technologies have developed a method to measure hemoglobin levels in small-volume blood samples.
By using 3D aerosol jet-printing to put perovskites on graphene, scientists have made X-ray detectors with record sensitivity that can greatly improve the efficiency and reduce the cost.
Researchers are developing technology to improve high-resolution bioimaging of structures and tissues located deep within the body.
