A scoping review on multimodal deep learning in biomedical images and texts

TL;DR

This scoping review summarizes the current state, applications, and research gaps of multimodal deep learning combining biomedical images and texts, aiming to advance diagnostic and interpretative systems.

Contribution

It provides a comprehensive overview of multimodal deep learning in biomedical data, identifying key concepts, study types, and future research directions.

Findings

Multimodal deep learning is applied to report generation, visual question answering, and diagnosis.

Research gaps include limited integration techniques and evaluation standards.

Diverse applications highlight the potential of MDL in biomedical fields.

Abstract

Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the integration of multiple sources of data, such as images and text, has the potential to revolutionize the analysis and interpretation of biomedical data. However, it only caught researchers' attention recently. To this end, there is a critical need to conduct a systematic review on this topic, identify the limitations of current work, and explore future directions. In this scoping review, we aim to provide a comprehensive overview of the current state of the field and identify key concepts, types of studies, and research gaps with a focus on biomedical images and texts joint learning, mainly because these two were the most commonly available data types in MDL research. This study reviewed the current uses of multimodal deep learning on five tasks: (1) Report generation, (2) Visual question answering, (3) Cross-modal retrieval, (4) Computer-aided diagnosis, and (5) Semantic segmentation. Our results highlight the diverse applications and potential of MDL and suggest directions for future research in the field. We hope our review will facilitate the collaboration of natural language processing (NLP) and medical imaging communities and support the next generation of decision-making and computer-assisted diagnostic system development.