Pixel Club: Embrace The Noise-Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays

Jonathan Laserson (Zebra Medical Vision)
Tuesday, 29.1.2019, 11:30
Room 337 Taub Bld.

The chest X-ray scan is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. It is also one of the hardest to interpret, with disagreement rating of around 30% even for experienced radiologists. At Zebra, we have access to millions of X-ray scans, as well as their accompanied anonymized textual reports written by hospital radiologists. Can this data be used to teach an algorithm to identify significant clinical findings from these scans? By manually tagging a relatively small set of sentences, we were able to construct a training set of almost 1M studies over the 40 most prevalent chest X-ray pathologies. A deep learning model was trained to predict the findings given the patient frontal and lateral scans. We compared the model's predictions to those made by a team of radiologists. Would the average radiologist agree more with his/her colleagues or with the model?

*Dr. Jonathan Laserson is the lead AI researcher at Zebra Medical Vision. He did his master and undergraduate studies in the Technion and has a PhD from the Computer Science AI lab at Stanford University. After a few years doing machine learning at Google and IBM, today he is focused on Deep Learning algorithms and their application to medical images understanding.

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