Contextualized Keyword Representations for Multi-modal Retinal Image Captioning

by   Jia-Hong Huang, et al.

Medical image captioning automatically generates a medical description to describe the content of a given medical image. A traditional medical image captioning model creates a medical description only based on a single medical image input. 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 the existing multi-modal medical image captioning dataset, experimental results show that the proposed model is effective with the increase of +53.2 state-of-the-art method.


Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"

Automatically generating medical reports for retinal images is one of th...

Multi-Modal Image Captioning for the Visually Impaired

One of the ways blind people understand their surroundings is by clickin...

A Synchronized Multi-Modal Attention-Caption Dataset and Analysis

In this work, we present a novel multi-modal dataset consisting of eye m...

Multi-Image Summarization: Textual Summary from a Set of Cohesive Images

Multi-sentence summarization is a well studied problem in NLP, while gen...

Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning

Significant progress has been made on visual captioning, largely relying...

Detecting Hate Speech in Multi-modal Memes

In the past few years, there has been a surge of interest in multi-modal...

Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing

Deep learning-based image processing is capable of creating highly appea...