# Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning

arXiv: https://arxiv.org/abs/2102.02959 · HTML: https://mlsanity.com/paper/2102.02959/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes](https://mlsanity.com/paper/2002.04752/) (2020)
- [Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation](https://mlsanity.com/paper/1603.08486/) (2016)
- [A Comparison of Word Embeddings for the Biomedical Natural Language Processing](https://mlsanity.com/paper/1802.00400/) (2018)
- [Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems](https://mlsanity.com/paper/1512.08756/) (2015)
- [TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays](https://mlsanity.com/paper/1806.02121/) (2018)

## Built on by (most cited first, 1 of 1)

- [What limits performance of weakly supervised deep learning for chest CT classification?](https://mlsanity.com/paper/2402.04419/) (2024)

## Abstract (arXiv)

Purpose: To develop high throughput multi-label annotators for body (chest, abdomen, and pelvis) Computed Tomography (CT) reports that can be applied across a variety of abnormalities, organs, and disease states. Approach: We used a dictionary approach to develop rule-based algorithms (RBA) for extraction of disease labels from radiology text reports. We targeted three organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) with four diseases per system based on their prevalence in our dataset. To expand the algorithms beyond pre-defined keywords, attention-guided recurrent neural networks (RNN) were trained using the RBA-extracted labels to classify reports as being positive for one or more diseases or normal for each organ system. Confounding effects on model performance were evaluated using random initialization or pre-trained embedding as well as different sizes of training datasets. Performance was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC) against 2,158 manually obtained labels. Results: Our models extracted disease labels from 261,229 radiology reports of 112,501 unique subjects. Pre-trained models outperformed random initialization across all diseases. As the training dataset size was reduced, performance was robust except for a few diseases with relatively small number of cases. Pre-trained classification AUCs achieved > 0.95 for all five disease outcomes across all three organ systems. Conclusions: Our label-extracting pipeline was able to encompass a variety of cases and diseases by generalizing beyond strict rules with exceptional accuracy. This method can be easily adapted to enable automated labeling of hospital-scale medical data sets for training image-based disease classifiers.