Computer assisted surgery (Abingdon, England)
Dec 26, 2025
Reinforcement learning (RL) has emerged as a powerful artificial intelligence paradigm in medical image analysis, excelling in complex decision-making tasks. This systematic review synthesizes the applications of RL across diverse imaging domains-inc...
The integration of diagnostic imaging with radiation therapy (RT) is evolving into a continuous workflow, significantly advancing personalised oncology care. Recent technological innovations, particularly the incorporation of real-time magnetic reson...
Journal of nuclear medicine technology
Dec 3, 2025
Cardiovascular diagnostic imaging plays a crucial role in modern health care, supporting accurate diagnosis, risk stratification, and the management of cardiovascular diseases. However, ensuring that imaging is used appropriately has become a key foc...
Disease detection using medical images enables early and precise diagnosis. Despite the growing success of deep learning models, accurate classification remains a significant challenge. Medical images often exhibit characteristics such as limited spa...
Medical image segmentation is crucial in accurately diagnosing diseases and assisting physicians in examining relevant areas. Therefore, there is a pressing need for an artificial intelligence-based model that can facilitate the diagnostic process an...
Phantom studies are essential in medical imaging, offering a controlled and reproducible framework for evaluating imaging technologies across all modalities. Phantoms, whether physical (synthetic, biological, or mixed) or computational, simulate huma...
With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT ...
Data heterogeneity critically limits distributed artificial intelligence (AI) in medical imaging. We propose HeteroSync Learning (HSL), a privacy-preserving framework that addresses heterogeneity through: (1) Shared Anchor Task (SAT) for cross-node r...
The heterogeneity of medical images poses significant challenges to accurate disease diagnosis. To tackle this issue, the impact of such heterogeneity on the causal relationship between image features and diagnostic labels should be incorporated into...
Medical image segmentation plays a vital role in diagnosis, treatment planning, and disease monitoring. However, endoscopic and dermoscopic images often exhibit blurred boundaries and low contrast, presenting a significant challenge for precise segme...
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