To develop a machine learning approach for classifying disease progression in chest radiographs using weak labels automatically derived from radiology reports.
In this retrospective study, a twin neural network was developed to classify anatomy-specific disease progression into four categories: improved, unchanged, worsened, and new. A two-step weakly-supervised learning approach was employed, pre-training the model on 243,008 frontal chest radiographs from 63,877 MIMIC-CXR patients (mean age 51.7 years; female 34,813), and fine-tuning it on the subset with progression labels derived from consecutive studies. Model performance was evaluated for six pathologic observations on test datasets of unseen MIMIC-CXR patients. Area under the receiver operating characteristic (AUC) analysis was used to evaluate classification performance. The algorithm is also capable of generating bounding-box predictions to localize areas of new progression. Recall, precision, and mean average precision (mAP) were used to evaluate the new progression localization. One-tailed paired t-tests were used to assess statistical significance.
The model outperformed most baselines in progression classification, achieving macro AUC scores of 0.72 ± 0.004 for atelectasis, 0.75 ± 0.007 for consolidation, 0.76 ± 0.017 for edema, 0.81 ± 0.006 for effusion, 0.7 ± 0.032 for pneumonia, and 0.69 ± 0.01 for pneumothorax. For new observation localization, the model achieved mAP scores of 0.25 ± 0.03 for atelectasis, 0.34 ± 0.03 for consolidation, 0.33 ± 0.03 for edema, and 0.31 ± 0.03 for pneumothorax.
Disease progression classification models were developed on a large chest radiograph dataset, which can be used to monitor interval changes and detect new pathologies on chest radiographs.
https://pubs.rsna.org/doi/10.1148/ryai.230277
@article{yu2024anatomy,
title={Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning},
author={Yu, Ke and Ghosh, Shantanu and Liu, Zhexiong and Deible, Christopher and Poynton, Clare B and Batmanghelich, Kayhan},
journal={Radiology: Artificial Intelligence},
pages={e230277},
year={2024},
publisher={Radiological Society of North America}
}
- MIMIC-CXR: a large dataset of chest radiographs in DICOM format with free-text radiology reports.
- RadGraph:a dataset of entities and relations in full-text radiology reports from MIMIC-CXR.
- ImaGenome: a dataset of bounding box annotations of anatomic parts in MIMIC-CXR chest radigraphs.
- Preprocessed data: preprocssed data files required for training and evaluation.
- Pre-trained AGXNet: pre-trained AGXNet checkpoint.
Train Siamese AGXNet
bash batch_run.sh
Evaluate trained model
bash batch_eval.sh
For any quries, contact Ke Yu (email: yu.ke@pitt.edu).