'Quantification of imaging data is a sector that has a very high potential in clinical research studies, because AI, in this setting, can be used to speed things up considerably,' he explained. QUIBIM works to accelerate workflows in pathology and radiology along four main axes: image reconstruction, segmentation, detection and data mining. AI could help significantly to reduce acquisition times, for example in MRI examinations, by using raw data generated by the imaging modalities. Image reconstruction is currently the main focus of investigation, and Alberich works on algorithms that process data using deep learning for under-sampled MRI reconstruction. 'Our aim is to identify all these regions, tissues and their potential variability.
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Researchers have developed a new technique based on artificial intelligence and machine learning, which enable clinicians to acquire higher quality images without having to collect additional data. A radiologist's ability to make accurate diagnoses from high-quality diagnostic imaging studies directly impacts patient outcome. However, acquiring sufficient data to generate the best quality imaging comes at a cost - increased radiation dose for computed tomography (CT) and positron emission tomography (PET) or uncomfortably long scan times for magnetic resonance imaging (MRI). Now researchers with the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH) have addressed this challenge with a new technique based on artificial intelligence and machine learning. They describe the technique - dubbed AUTOMAP (automated transform by manifold approximation) - in a paper published today in the journal Nature.
Computer vision will play a crucial role in visual search, self-driving cars, medicine and many other applications. Success will hinge on collecting and labeling large labeled datasets which will be used to train and test new algorithms. One area that has seen great advances over the last five years is image classification i.e. determining automatically what objects are present in an image. Existing image classification datasets have an equal number of images for each class. However, the real world is long tailed: only a small percentage of classes are likely to be observed; most classes are infrequent or rare.