Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
This work addresses the problem of automated medical image annotation for radiology, representing an incremental advance by applying existing deep learning methods to a new domain-specific dataset.
The paper tackled automated annotation of chest X-rays by detecting diseases and describing their contexts, achieving significantly improved results through a recurrent neural cascade model that incorporates joint image/text contexts.
Despite the recent advances in automatically describing image contents, their applications have been mostly limited to image caption datasets containing natural images (e.g., Flickr 30k, MSCOCO). In this paper, we present a deep learning model to efficiently detect a disease from an image and annotate its contexts (e.g., location, severity and the affected organs). We employ a publicly available radiology dataset of chest x-rays and their reports, and use its image annotations to mine disease names to train convolutional neural networks (CNNs). In doing so, we adopt various regularization techniques to circumvent the large normal-vs-diseased cases bias. Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features. Moreover, we introduce a novel approach to use the weights of the already trained pair of CNN/RNN on the domain-specific image/text dataset, to infer the joint image/text contexts for composite image labeling. Significantly improved image annotation results are demonstrated using the recurrent neural cascade model by taking the joint image/text contexts into account.