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Ronald M Summers

Showing results (131-140 of 377) with videos related to

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Medical Image Analysis|November 17, 2023
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentationBoah Kim, Yujin Oh, Bradford J Wood, et al.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|February 8, 2014
Visual phrase learning and its application in computed tomographic colonographyShijun Wang, Matthew McKenna, Zhuoshi Wei, et al.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|January 5, 2013
Gaussian process inference for estimating pharmacokinetic parameters of dynamic contrast-enhanced MR imagesShijun Wang, Peter Liu, Baris Turkbey, et al.
Magnetic Resonance in Medicine|June 10, 2021
Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combinationKerstin Hammernik, Jo Schlemper, Chen Qin, et al.
Applications of Medical Artificial Intelligence : Second International Workshop, AMAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings|May 9, 2025
Anatomical Location-Guided Deep Learning-Based Genetic Cluster Identification of Pheochromocytomas and Paragangliomas From CT ImagesBikash Santra, Abhishek Jha, Pritam Mukherjee, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|March 15, 2025
A unified approach to medical image segmentation by leveraging mixed supervision and self and transfer learning (MIST)Jianfei Liu, Sayantan Bhadra, Omid Shafaat, et al.
Abdominal Radiology (New York)|October 31, 2023
Longitudinal follow-up of incidental renal calculi on computed tomographyPritam Mukherjee, Sungwon Lee, Daniel C Elton, et al.
Arxiv|October 4, 2023
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentationBoah Kim, Yujin Oh, Bradford J Wood, et al.
Radiology. Artificial Intelligence|August 5, 2021
Use of Variational Autoencoders with Unsupervised Learning to Detect Incorrect Organ Segmentations at CTVeit Sandfort, Ke Yan, Peter M Graffy, et al.
International Journal of Computer Assisted Radiology and Surgery|November 5, 2022
Universal lymph node detection in T2 MRI using neural networksTejas Sudharshan Mathai, Sungwon Lee, Thomas C Shen, et al.
Pageof 38

Showing results (131-140 of 377) with videos related to

Sort By:
Pageof 38
Medical Image Analysis|November 17, 2023
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentationBoah Kim, Yujin Oh, Bradford J Wood, et al.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|February 8, 2014
Visual phrase learning and its application in computed tomographic colonographyShijun Wang, Matthew McKenna, Zhuoshi Wei, et al.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|January 5, 2013
Gaussian process inference for estimating pharmacokinetic parameters of dynamic contrast-enhanced MR imagesShijun Wang, Peter Liu, Baris Turkbey, et al.
Magnetic Resonance in Medicine|June 10, 2021
Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combinationKerstin Hammernik, Jo Schlemper, Chen Qin, et al.
Applications of Medical Artificial Intelligence : Second International Workshop, AMAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings|May 9, 2025
Anatomical Location-Guided Deep Learning-Based Genetic Cluster Identification of Pheochromocytomas and Paragangliomas From CT ImagesBikash Santra, Abhishek Jha, Pritam Mukherjee, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|March 15, 2025
A unified approach to medical image segmentation by leveraging mixed supervision and self and transfer learning (MIST)Jianfei Liu, Sayantan Bhadra, Omid Shafaat, et al.
Abdominal Radiology (New York)|October 31, 2023
Longitudinal follow-up of incidental renal calculi on computed tomographyPritam Mukherjee, Sungwon Lee, Daniel C Elton, et al.
Arxiv|October 4, 2023
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentationBoah Kim, Yujin Oh, Bradford J Wood, et al.
Radiology. Artificial Intelligence|August 5, 2021
Use of Variational Autoencoders with Unsupervised Learning to Detect Incorrect Organ Segmentations at CTVeit Sandfort, Ke Yan, Peter M Graffy, et al.
International Journal of Computer Assisted Radiology and Surgery|November 5, 2022
Universal lymph node detection in T2 MRI using neural networksTejas Sudharshan Mathai, Sungwon Lee, Thomas C Shen, et al.
Pageof 38