New Artificial Intelligence Technique Dramatically Improves the Quality of Medical Imaging

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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.


Kaggle Image Competitions! How to Deal with Large Datasets

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When I have to deal with Huge image datasets, this is what I do. Working with image datasets in Kaggle competitions can be quite problematic, your computer could just freeze and don't care about you anymore. To stop this things from happening, I'm going to be sharing with you here the 5 Major Steps to work with Image datasets.


iNaturalist 2018 Species Classification Competition

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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.


New artificial intelligence technique dramatically improves the quality of medical imaging

#artificialintelligence

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, enabling clinicians to acquire higher quality images without having to collect additional data. They describe the technique - dubbed AUTOMAP (automated transform by manifold approximation) - in a paper published today in the journal Nature. "An essential part of the clinical imaging pipeline is image reconstruction, which transforms the raw data coming off the scanner into images for radiologists to evaluate," says Bo Zhu, PhD, a research fellow in the MGH Martinos Center and first author of the Nature paper.