Title | Automatic Classification and Reporting of Multiple Common Thorax Diseases Using Chest Radiographs |
Publication Type | Book Chapter |
Year of Publication | 2019 |
Authors | Wang X, Peng Y, Lu L, Lu Z, Summers RM |
Book Title | Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Advances in Computer Vision and Pattern Recognition |
Chapter | Automatic Classification and Reporting of Multiple Common Thorax Diseases Using Chest Radiographs |
Pagination | 393-412 |
Publisher | Springer, Cham |
ISBN Number | 978-3-030-13968-1 |
Abstract | Chest X-rays are one of the most common radiological examinations in daily clinical routines. Reporting thorax diseases using chest X-rays is often an entry-level task for radiologist trainees. Yet, reading a chest X-ray image remains a challenging job for learning-oriented machine intelligence, due to (1) shortage of large-scale machine-learnable medical image datasets, and (2) lack of techniques that can mimic the high-level reasoning of human radiologists that requires years of knowledge accumulation and professional training. In this paper, we show the clinical free-text radiological reports that accompany X-ray images in hospital picture and archiving communication systems can be utilized as a priori knowledge for tackling these two key problems. We propose a novel text-image embedding network (TieNet) for extracting the distinctive image and text representations. Multi-level attention models are integrated into an end-to-end trainable CNN-RNN architecture for highlighting the meaningful text words and image regions. We first apply TieNet to classify the chest X-rays by using both image features and text embeddings extracted from associated reports. The proposed auto-annotation framework achieves high accuracy (over 0.9 on average in AUCs) in assigning disease labels for our hand-label evaluation dataset. Furthermore, we transform the TieNet into a chest X-ray reporting system. It simulates the reporting process and can output disease classification and a preliminary report together, with X-ray images being the only input. The classification results are significantly improved (6% increase on average in AUCs) compared to the state-of-the-art baseline on an unseen and hand-labeled dataset (OpenI). |
DOI | 10.1007/978-3-030-13969-8_19 |