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Feb 14 2018 | Deep Learning in Neuroradiology | Posted In: News
SUMMARY: Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly.
Dr. Curtis Langlotz speaking to the National Academy of Medicine's National Cancer Policy Forum on how artificial intelligence will change imaging diagnosis of cancer  
Jan 12 2018 | Posted In: News
In PET imaging, the amount of radiotracer dose typically correlates with the level of image quality.  But researchers from Stanford University have trained a deep-learning algorithm to process ultralow-dose PET image data and then create synthetic images that approximate PET scans acquired using a standard radiotracer dose.
Dec 21 2017 | Posted In: News
Artificial intelligence (AI) is here to stay in radiology—and so are radiologists. Curtis Langlotz, MD, PhD, of Stanford made the case during a how-to session Nov. 27 at the annual meeting of the Radiological Society of North America in Chicago.
Nov 28 2017 | Posted In: News
The test set of hand x-ray images from the 2017 RSNA Bone Age Challenge has been released on the AIMI website - click on the datasets page to access!