Learning to diagnose common thorax diseases on chest radiographs from
radiology reports in Vietnamese
- URL: http://arxiv.org/abs/2209.04794v1
- Date: Sun, 11 Sep 2022 06:06:03 GMT
- Title: Learning to diagnose common thorax diseases on chest radiographs from
radiology reports in Vietnamese
- Authors: Thao T.B. Nguyen, Tam M. Vo, Thang V. Nguyen, Hieu H. Pham, Ha Q.
Nguyen
- Abstract summary: We propose a data collecting and annotation pipeline that extracts information from Vietnamese radiology reports to provide accurate labels for chest X-ray (CXR) images.
This can benefit Vietnamese radiologists and clinicians by annotating data that closely match their endemic diagnosis categories which may vary from country to country.
- Score: 0.33598755777055367
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a data collecting and annotation pipeline that extracts
information from Vietnamese radiology reports to provide accurate labels for
chest X-ray (CXR) images. This can benefit Vietnamese radiologists and
clinicians by annotating data that closely match their endemic diagnosis
categories which may vary from country to country. To assess the efficacy of
the proposed labeling technique, we built a CXR dataset containing 9,752
studies and evaluated our pipeline using a subset of this dataset. With an
F1-score of at least 0.9923, the evaluation demonstrates that our labeling tool
performs precisely and consistently across all classes. After building the
dataset, we train deep learning models that leverage knowledge transferred from
large public CXR datasets. We employ a variety of loss functions to overcome
the curse of imbalanced multi-label datasets and conduct experiments with
various model architectures to select the one that delivers the best
performance. Our best model (CheXpert-pretrained EfficientNet-B2) yields an
F1-score of 0.6989 (95% CI 0.6740, 0.7240), AUC of 0.7912, sensitivity of
0.7064 and specificity of 0.8760 for the abnormal diagnosis in general.
Finally, we demonstrate that our coarse classification (based on five specific
locations of abnormalities) yields comparable results to fine classification
(twelve pathologies) on the benchmark CheXpert dataset for general anomaly
detection while delivering better performance in terms of the average
performance of all classes.
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