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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

There is a critical need to bridge the "visual-pathological gap," as many standard models lack the ability to accurately describe pathological locations. 126287

“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 This review provides a systematic and comprehensive analysis

“Modern deep learning-based approaches have supplanted traditional approaches in image captioning, leading to more efficient and sophisticated models.” ScienceDirect.com The review highlights the primary obstacles currently facing

The identifier refers to the specific article index for a prominent scientific review titled "Deep image captioning: A review of methods, trends and future challenges" , published in the journal Neurocomputing (Volume 546, August 2023).

The review highlights the primary obstacles currently facing researchers in the field: