- A survey on deep learning in medical image registration: New . . .
These advancements have not only enriched the field of image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D–3D registration
- Deep learning in medical image registration: introduction and survey
Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis
- Deep Learning in Medical Image Analysis: A Survey - IEEE Xplore
One of the major applications of deep learning is image processing, which, when integrated with the data received from medical imaging, has the ability to improve the diagnostic process by reducing analysis time and providing accurate results
- Deep learning in medical image registration: a survey
In this section, we summarize the current research trends and future directions of deep learning in medical image registration As we can see from Fig 2, some research trends have emerged
- Deep Learning for Medical Image Registration: A Comprehensive Review
In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models This paper provides a comprehensive review of medical image registration
- Deep Learning Applications in Medical Image Segmentation: Overview . . .
Apply revolutionary deep learning technology to the fast-growing field of medical image segmentation Precise medical image segmentation is rapidly becoming one of the most important tools in medical research, diagnosis, and treatment
- Recent advances and clinical applications of deep learning in medical . . .
Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration
- Deep Learning-Based Medical Image Registration Algorithm: Enhancing . . .
Overall, by leveraging deep learning technologies and innovative algorithmic structures, this study addresses pivotal challenges in medical image registration, offering more precise and dependable support for clinical applications like surgical navigation and tumor surveillance
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