Semantic Textual Similarity Assessment in Chest X-ray Reports Using a
  Domain-Specific Cosine-Based Metric
Sayeh Gholipour Picha Dawood Al Chanti Alice Caplier
Medical language processing and deep learning techniques have emerged as
critical tools for improving healthcare, 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.