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
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).
Traditional training data can lead to hallucinations or biased outputs, particularly in socio-economically diverse content.
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:
Using attention mechanisms to identify the most relevant parts of an image for a specific description.
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
