There is a critical need to bridge the "visual-pathological gap," as many standard models lack the ability to accurately describe pathological locations.
“Despite the great progress made by existing deep generation methods, it is still inadequate in (1) insufficient consideration of the visual-pathological gap and (2) weak evaluation of clinical language style.” National Institutes of Health (.gov) · 4 months ago 126287
Metrics like BLEU and ROUGE are used to measure accuracy, but they sometimes struggle to capture the full semantic meaning or clinical relevance of a caption. There is a critical need to bridge the
Using attention mechanisms to identify the most relevant parts of an image for a specific description. This review provides a systematic and comprehensive analysis
This review provides a systematic and comprehensive analysis of how deep learning models translate visual content into human language, with a particular focus on both general and medical applications. 🔬 Core Components of the Review
A significant portion of the review and subsequent research citing it (like work on uterine ultrasound captioning ) focuses on "computer-aided diagnosis". Key insights include: