Medical image processing on the GPU - Past, present and future
By Anders Eklund, Paul Dufort, Daniel Forsberg, Stephen LaConte. 2013
415 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Salvador Elías Venegas-Andraca
- Thomas Martin Deserno
- Sanjay Agrawal
- Ajith Abraham
- Lingraj Dora
- Benjamin Berkels
- Mohamad Forouzanfar
- Shiva Maleki Varnosfaderani
In Radiology, Nuclear Medicine and Imaging
- Thomas Martin Deserno
- Benjamin Berkels
- Mohamad Forouzanfar
- Shiva Maleki Varnosfaderani
- András P. Keszei
- Arvid Lundervold
Citations in the corpus
- State-of-the-Art Methods for Brain Tissue Segmentation: A Review (2017, background): “Nowadays, CNNs are gaining more attention for brain tissue segmentation with efficient hardware implementation [121], [122].”
- Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates (2015, neutral): “However, this increased processing time is not a problem in practice, as for typical sample sizes a desktop computer can run a permutation test for neuroimaging data in less than a minute (27, 43).”
- A survey of quantum image representations (2015, background): “, digital cameras: In less than twenty years, products on this market ranged from image size of 756 × 504 pixels and 500 g of weight (Kodak DC40, Table I of [1]) to image size of 18 megapixels and 245 g of weight [15] ) or as key technical requirements within a given domain of application (for examp”
- Survey of Non-Rigid Registration Tools in Medicine (2017, background): “[25], while others emphasize high-performance computing (HPC) [26], metaheuristics such as evolutionary algorithms [27], and implementations on the graphical processing units [28, 29].”
- Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique (2016, neutral): “much later for machine learning [9], [10].”
- The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, methodology): “Additionally, GPUs can enhance the operation of different AI algorithms used in medical imaging tasks like image registration, image segmentation, image denoising, and image classification [113].”