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There is a critical need to bridge the "visual-pathological gap," as many standard models lack the ability to accurately describe pathological locations.

“Modern deep learning-based approaches have supplanted traditional approaches in image captioning, leading to more efficient and sophisticated models.” ScienceDirect.com 126287

Traditional training data can lead to hallucinations or biased outputs, particularly in socio-economically diverse content. There is a critical need to bridge the

Newer models like JAGAN (Joint Attention Generative Adversarial Nets) are introduced to ensure that the generated text maintains a professional "clinical language style". 📊 Key Challenges & Metrics 📊 Key Challenges & Metrics “Despite the great

“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

Using attention mechanisms to identify the most relevant parts of an image for a specific description.

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