Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric

Sayeh Gholipour Picha, Dawood Al Chanti, Alice Caplier

2024

Abstract

Medical language processing and deep learning techniques have emerged as critical tools for improving health-care, particularly in the analysis of medical imaging and medical text data. These multimodal data fusion techniques help to improve the interpretation of medical imaging and lead to increased diagnostic accuracy, informed clinical decisions, and improved patient outcomes. The success of these models relies on the ability to extract and consolidate semantic information from clinical text. This paper addresses the need for more robust methods to evaluate the semantic content of medical reports. Conventional natural language processing approaches and metrics are initially designed for considering the semantic context in the natural language domain and machine translation, often failing to capture the complex semantic meanings inherent in medical content. In this study, we introduce a novel approach designed specifically for assessing the semantic similarity between generated medical reports and the ground truth. Our approach is validated, demonstrating its efficiency in assessing domain-specific semantic similarity within medical contexts. By applying our metric to state-of-the-art Chest X-ray report generation models, we obtain results that not only align with conventional metrics but also provide more contextually meaningful scores in the considered medical domain.

Paper Citation

in Harvard Style

Gholipour Picha S., Al Chanti D. and Caplier A. (2024). Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-688-0, SciTePress, pages 487-494. DOI: 10.5220/0012429600003657

in Bibtex Style

@conference{bioinformatics24,
author={Sayeh Gholipour Picha and Dawood Al Chanti and Alice Caplier},
title={Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2024},
pages={487-494},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012429600003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric
SN - 978-989-758-688-0
AU - Gholipour Picha S.
AU - Al Chanti D.
AU - Caplier A.
PY - 2024
SP - 487
EP - 494
DO - 10.5220/0012429600003657
PB - SciTePress