  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
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. 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).
