Posted by Zaid Nabulsi, Software Engineer and Po-Hsuan Cameron Chen, Software Engineer, Google Health
The adoption of machine learning (ML) for medical imaging applications presents an exciting opportunity to improve the availability, latency, accuracy, and consistency of chest X-ray (CXR) image interpretation. Indeed, a plethora of algorithms have already been developed to detect specific conditions, such as lung cancer, tuberculosis and pneumothorax. By virtue of being trained to detect a specific disease, however, the utility of these algorithms may be limited in a general clinical setting, where a wide variety of abnormalities could surface. For example, a pneumothorax detector is not expected to highlight nodules suggestive of cancer, and a tuberculosis detector may not identify findings specific to pneumonia. Since an initial triaging step is to determine whether a CXR contains any concerning abnormalities, a general-purpose algorithm that identifies X-rays containing any sort of abnormality could significantly facilitate the workflow. However, developing a classifier to detect any abnormality is challenging due to the wide variety of abnormal findings that present on CXRs.
In “Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Two Unseen Diseases Tuberculosis and COVID-19”, published in Scientific Reports, we present a model that can distinguish between normal and abnormal CXRs across multiple de-identified datasets and settings. We find that the model performs well on general abnormalities, as well as unseen examples of tuberculosis and COVID-19. We are also releasing our set of radiologists’ labels1 for the test set used in this study for the publicly available ChestX-ray14 dataset.
A Deep Learning System for Detecting Abnormal Chest X-rays
The deep learning system we used is based on the EfficientNet-B7 architecture, pre-trained on ImageNet. We trained the model using over 200,000 de-identified CXRs from the Apollo Hospitals in India. Each CXR was assigned a label of either “normal” or “abnormal” using a regular expression–based natural language processing approach
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