Learning to estimate label uncertainty for automatic radiology report parsing
arXiv:1910.00673
Abstract
Bootstrapping labels from radiology reports has become the scalable alternative to provide inexpensive ground truth for medical imaging. Because of the domain specific nature, state-of-the-art report labeling tools are predominantly rule-based. These tools, however, typically yield a binary 0 or 1 prediction that indicates the presence or absence of abnormalities. These hard targets are then used as ground truth to train image models in the downstream, forcing models to express high degree of certainty even on cases where specificity is low. This could negatively impact the statistical efficiency of image models. We address such an issue by training a Bidirectional Long-Short Term Memory Network to augment heuristic-based discrete labels of X-ray reports from all body regions and achieve performance comparable or better than domain-specific NLP, but with additional uncertainty estimates which enable finer downstream image model training.
arXiv commentMed-NeurIPS-2019 (accepted)
References in corpus (8)
- Adam: A Method for Stochastic Optimization
- Distilling the Knowledge in a Neural Network
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
- Learning to diagnose from scratch by exploiting dependencies among labels
- Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions
- Caveats in Generating Medical Imaging Labels from Radiology Reports
- A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging
- Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction