Medical image captioning automatically generates a medical description to describe the content of a given medical image. Traditional medical image captioning models create a medical description based on a single medical image input only. Hence, an abstract medical description or concept is hard to be generated based on the traditional approach. Such a method limits the effectiveness of medical image captioning. Multi-modal medical image captioning is one of the approaches utilized to address this problem. In multi-modal medical image captioning, textual input, e.g., expert-defined keywords, is considered as one of the main drivers of medical description generation. Thus, encoding the textual input and the medical image effectively are both important for the task of multi-modal medical image captioning. In this work, a new end-to-end deep multi-modal medical image captioning model is proposed. Contextualized keyword representations, textual feature reinforcement, and masked self-attention are used to develop the proposed approach. Based on the evaluation of an existing multi-modal medical image captioning dataset, experimental results show that the proposed model is effective with an increase of +53.2% in BLEU-avg and +18.6% in CIDEr, compared with the state-of-the-art method. https://github.com/Jhhuangkay/Contextualized-Keyword-Representations-for-Multi-modal-Retinal-Image-Captioning
|Original language||English (US)|
|Title of host publication||Proceedings of the 2021 International Conference on Multimedia Retrieval|
|Number of pages||8|
|State||Published - Aug 21 2021|
Bibliographical noteKAUST Repository Item: Exported on 2021-10-07
Acknowledgements: This work is supported by competitive research funding from King Abdullah University of Science and Technology (KAUST) and University of Amsterdam.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.