Aug 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 55 references
TL;DR
A structured analysis and multi-dimensional taxonomy of DRL applications in medical imaging, highlighting the advantages and limitations of existing approaches and organizing the literature based on multiple dimensions, including medical data type, task category, and DRL techniques.
Major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications are summarized and emerging directions such as self-supervised learning, Explainable AI, federated learning, and lightweight models are h...
Lakshmi Sai Anusha Dadi, Pravallika Devi Kommana· International Journal for Re...· 0 citations
Traditional diagnostic methods in biomedicine are often constrained by subjectivity, misdiagnosis, and the inability to efficiently process large, complex, and multimodal datasets. Recent advances in deep learning and hybrid architectures have enabled the automated learning of discriminative representations from high‐d...
Li-Feng Li, Ashfaque Khowaja, Yu-Cheng Song et al.· Medicine Advances· 0 citations
This review systematically explores deep learning‐driven advancements, focusing on their theoretical foundations, key technological breakthroughs and typical application scenarios, and systematically categorizes fusion architectures into data‐level, feature‐level, and decision‐level hierarchies across diverse modalitie...
Haohan Wu, Yaping Xu, Zhiduo Zhang et al.· WIREs Data Mining and Knowle...· 0 citations
This review provides a comprehensive and structured synthesis of FMs in medical image analysis by systematically organizing studies into two primary categories: vision-only foundation models (VFMs) and vision-language foundation models (VLFMs), based on their architectural foundations, training strategies, and downstre...
P. Rajendran, M. Safari, Wen-Feng He et al.· Medical Image Analysis· 0 citations
Modern Medical Imaging methods like X-ray, Ultrasound, CT scan, and MRI are important technologies used to diagnose various diseases since they help healthcare professionals identify the presence of specific structures inside patient's bodies. However, these modern techniques may produce unreliable results that lead to...
Atik Naikwade, Sarvesh Chowdary· International Symposium on N...· 0 citations
RL in ultrasound imaging is an emerging field of research and has advantages for sequential workflow optimization tasks, but most approaches are at an early stage and have been tested on small datasets, lack consistent evaluation protocols, and report limited clinical translation.
Maha Ezzelarab, M. Madhusoodanan, Shrimanti Ghosh et al.· Applied Biosciences· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
