Deep learning, a subset of machine learning, draws on artificial neural networks that are designed to imitate how humans think and learn. We report the use in disease diagnostic, e.g. in imaging, research and its application in technologies such as robotics or wearables.
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.
Using a deep learning algorithm, researchers have developed a way to accurately predict which skin cancers are highly metastatic.
A new approach to tackling the spread of malaria in sub-Saharan Africa, which combines affordable, easy-to-administer blood tests with machine learning and unbreakable encryption, has generated encouraging early results in Uganda.
Researchers have developed a rapid and cost-effective particle agglutination based sensor that is powered by holographic imaging and deep learning
Researchers have developed a smart functional robot that realized simultaneous disinfection of both air and object surface.
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.
Scientists have created a deep learning method, RoseTTAFold, to provide access to highly accurate protein structure prediction.
Students at TU Eindhoven developed the world's first interactive drone that can transmit emotions.
Engineers use Frontera supercomputer to develop physics-informed neural networks for additive manufacturing.
Using fluoresence images from live cells, researchers have trained an artificial neural network to reliably recognize cells that are infected by adenoviruses or herpes viruses.
New technology could transform the ability to accurately interpret HIV test results, particularly in low- and middle-income countries.
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 use AI to teach robots to make appropriate reactive human facial expressions, an ability that could build trust between humans and their robotic co-workers and care-givers.
A team of engineers from Rensselaer Polytechnic Institute and clinicians from Massachusetts General Hospital developed a deep learning algorithm that can help assess a patient's risk of cardiovascular disease with the same low-dose computerized tomography (CT) scan used to screen for lung cancer.
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.
Researchers propose a deep learning-based model for mimicking and continuously modifying speaker voice identity during speech translation.
Scientists at Osaka University employed deep learning to improve mobile mixed reality generation.
Deep learning technique optimizes the arrangement of sensors on a robot’s body to ensure efficient operation.
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.
Using AI and mobile digital microscopy, researchers hope to create screening tools that can detect precursors to cervical cancer in women in resource-limited settings.
Researchers are developing exoskeletons and prosthetic legs capable of thinking and making control decisions on their own using AI technology.
Researchers have evaluated whether data derived solely from these wristbands could accurately predict various types of seizures in pediatric patients.
Researchers have developed a deep learning tool that offers new opportunities for analyzing images taken with microscopes.
Researchers have developed a new soft tactile sensor with skin-comparable characteristics.
AI is helping researchers decipher images from a new holographic microscopy technique needed to investigate a key process in cancer immunotherapy “live” as it takes place.
Deep learning-based system enables dermatologist-level identification of suspicious skin lesions from smartphone photos, allowing better screening.
Researchers have analysed whether better design of deep learning studies can lead to the faster transformation of medical practices.
