This post discusses our open access journal paper. Imagine you are in an emergency department taking care of a patient who is having difficulty speaking. You want to ensure that their symptoms are not due to a stroke. You order a CT angiogram of the head and neck to evaluate…

In radiology, we’d like deep learning models to identify patterns in imaging that suggest disease. For example, to detect pneumonia (lung infection), we’d like them to identify patterns in the lung that indicate the presence of an active infection. But do we know that is what they’re actually doing? My…

I am sharing on GitHub PyTorch code to reproduce the results of CheXNet. CheXNet, the paper from Rajpurkar et al., predicted 14 common diagnoses using convolutional neural networks in over 100,000 NIH chest x-rays. Radiologists and other interested individuals, regardless of deep learning experience, can explore the model’s predictions and…

Having spent the last few days at RSNA 2017, I had a chance to see many of the different deep learning based prototypes for clinical decision support that vendors are offering. It’s impressive, and I am excited to see this technology get built into real, deployable products that can improve…