Medical image captioning models generate text to describe the semantic contents of an image, aiding the non-experts in understanding and interpretation. We propose a weakly-supervised approach to improve the performance of image captioning models on small image-text datasets by leveraging a large anatomically-labelled image classification dataset. Our method generates pseudo-captions (weak labels) for caption-less but anatomically-labelled (class-labelled) images using an encoder-decoder sequence-to-sequence model. The augmented dataset is used to train an image-captioning model in a weakly supervised learning manner. For fetal ultrasound, we demonstrate that the proposed augmentation approach outperforms the baseline on semantics and syntax-based metrics, with nearly twice as much improvement in value on BLEU-1 and ROUGE-L. Moreover, we observe that superior models are trained with the proposed data augmentation, when compared with the existing regularization techniques. This work allows seamless automatic annotation of images that lack human-prepared descriptive captions for training image-captioning models. Using pseudo-captions in the training data is particularly useful for medical image captioning when significant time and effort of medical experts is required to obtain real image captions.
Keywords: Data Augmentation; Fetal Ultrasound; Image Captioning.