Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset
This work addresses the challenge of limited labeled data in medical imaging for tuberculosis detection, offering a domain-specific pre-training approach that enhances generalization, though it is incremental as it builds on existing methods.
The authors tackled the problem of improving tuberculosis detection in chest X-rays by learning domain-specific features from a large hospital-scale dataset, achieving state-of-the-art performance in age and gender estimation and better transfer learning results on small-scale TB datasets.
The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks. The recent emergence of a large Chest X-ray dataset opened the possibility for learning features that are specific to the X-ray analysis task. In this work, we demonstrate that the features learned allow for better classification results for the problem of Tuberculosis detection and enable generalization to an unseen dataset. To accomplish the task of feature learning, we train a DenseNet-121 CNN on 112K images from the ChestXray14 dataset which includes labels of 14 common thoracic pathologies. In addition to the pathology labels, we incorporate metadata which is available in the dataset: Patient Positioning, Gender and Patient Age. We term this architecture MetaChexNet. As a by-product of the feature learning, we demonstrate state of the art performance on the task of patient Age \& Gender estimation using CNN's. Finally, we show the features learned using ChestXray14 allow for better transfer learning on small-scale datasets for Tuberculosis.