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

arXiv: https://arxiv.org/abs/1806.02121 · HTML: https://mlsanity.com/paper/1806.02121/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Contextualized Keyword Representations for Multi-modal Retinal Image Captioning](https://mlsanity.com/paper/2104.12471/) (2021)
- [Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"](https://mlsanity.com/paper/2105.14538/) (2021)
- [A Novel Evaluation Framework for Image2Text Generation](https://mlsanity.com/paper/2408.01723/) (2024)
- [A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data](https://mlsanity.com/paper/2405.12833/) (2024)
- [Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning](https://mlsanity.com/paper/2102.02959/) (2021)
- [XVertNet: Unsupervised Contrast Enhancement of Vertebral Structures with Dynamic Self-Tuning Guidance and Multi-Stage Analysis](https://mlsanity.com/paper/2306.03983/) (2023)
- [Deep Attention Q-Network for Personalized Treatment Recommendation](https://mlsanity.com/paper/2307.01519/) (2023)

## Abstract (arXiv)

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.