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arxiv: 1806.02121 · v1 · pith:WZ2U6UT6new · submitted 2018-06-06 · 💻 cs.CV · stat.ML

TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays

Jonathan Laserson , Christine Dan Lantsman , Michal Cohen-Sfady , Itamar Tamir , Eli Goz , Chen Brestel , Shir Bar , Maya Atar
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Eldad Elnekave
This is my paper
classification 💻 cs.CV stat.ML
keywords chestfindingsmodelradiologistsreportsableaccurateagree
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The chest X-ray (CXR) is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. There is an immense world-wide shortage of physicians capable of providing rapid and accurate interpretation of this study. A radiologist-driven analysis of over two million CXR reports generated an ontology including the 40 most prevalent pathologies on CXR. By manually tagging a relatively small set of sentences, we were able to construct a training set of 959k studies. A deep learning model was trained to predict the findings given the patient frontal and lateral scans. For 12 of the findings we compare the model performance against a team of radiologists and show that in most cases the radiologists agree on average more with the algorithm than with each other.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays

    cs.CV 2019-06 unverdicted novelty 7.0

    The paper introduces a new benchmark dataset with 73 sentence-level descriptors for AP chest X-ray findings obtained via crowdsourcing and demonstrates deep learning classifiers trained on them.

  2. Improved ICH classification using task-dependent learning

    cs.CV 2019-06 unverdicted novelty 4.0

    BloodNet improves ICH classification by modeling dependency between segmentation and classification tasks, reporting AUCs of 0.9493 and 0.9566 on held-out sets of over 1400 studies from more than 10 hospitals.