  The image captioning task is increasingly prevalent in artificial
intelligence applications for medicine. One important application is clinical
report generation from chest radiographs. The clinical writing of unstructured
reports is time consuming and error-prone. An automated system would improve
standardization, error reduction, time consumption, and medical accessibility.
In this paper we demonstrate the importance of domain specific pre-training and
propose a modified transformer architecture for the medical image captioning
task. To accomplish this, we train a series of modified transformers to
generate clinical reports from chest radiograph image input. These modified
transformers include: a meshed-memory augmented transformer architecture with
visual extractor using ImageNet pre-trained weights, a meshed-memory augmented
transformer architecture with visual extractor using CheXpert pre-trained
weights, and a meshed-memory augmented transformer whose encoder is passed the
concatenated embeddings using both ImageNet pre-trained weights and CheXpert
pre-trained weights. We use BLEU(1-4), ROUGE-L, CIDEr, and the clinical
CheXbert F1 scores to validate our models and demonstrate competitive scores
with state of the art models. We provide evidence that ImageNet pre-training is
ill-suited for the medical image captioning task, especially for less frequent
conditions (eg: enlarged cardiomediastinum, lung lesion, pneumothorax).
Furthermore, we demonstrate that the double feature model improves performance
for specific medical conditions (edema, consolidation, pneumothorax, support
devices) and overall CheXbert F1 score, and should be further developed in
future work. Such a double feature model, including both ImageNet pre-training
as well as domain specific pre-training, could be used in a wide range of image
captioning models in medicine.
