Narrative Review
Narrative reviews synthesize existing research to provide a comprehensive overview of a specific topic, aiming to identify key trends, gaps, and future research directions. Current research focuses on applying narrative reviews across diverse fields, employing various model architectures like graph neural networks, large language models, and diffusion models to analyze complex data and improve model interpretability and efficiency. This approach is crucial for advancing scientific understanding and informing the development of practical applications in areas such as medicine, engineering, and manufacturing.
Papers
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review
Masatoshi Uehara, Yulai Zhao, Tommaso Biancalani, Sergey Levine
A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging
Elizaveta Lavrova, Henry C. Woodruff, Hamza Khan, Eric Salmon, Philippe Lambin, Christophe Phillips
From A-to-Z Review of Clustering Validation Indices
Bryar A. Hassan, Noor Bahjat Tayfor, Alla A. Hassan, Aram M. Ahmed, Tarik A. Rashid, Naz N. Abdalla
Evaluating Human-AI Collaboration: A Review and Methodological Framework
George Fragiadakis, Christos Diou, George Kousiouris, Mara Nikolaidou
Neuromorphic Perception and Navigation for Mobile Robots: A Review
A. Novo, F. Lobon, H. G. De Marina, S. Romero, F. Barranco
AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review
Rui Jin, Derun Li, Dehui Xiang, Lei Zhang, Hailing Zhou, Fei Shi, Weifang Zhu, Jing Cai, Tao Peng, Xinjian Chen