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Yoshihisa Shinagawa

Showing results (1-10 of 9) with videos related to

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IEEE Transactions on Pattern Analysis and Machine Intelligence|September 13, 2008
Homotopic image pseudo-invariants for openset object recognition and image retrievalYoshihisa Shinagawa
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|May 17, 2008
Wavelet-based joint estimation and encoding of depth-image-based representations for free-viewpoint renderingMatthieu Maitre, Yoshihisa Shinagawa, Minh N Do
IEEE Transactions on Bio-Medical Engineering|September 30, 2005
Simulating pathological gait using the enhanced linear inverted pendulum modelTaku Komura, Akinori Nagano, Howard Leung, et al.
NEJM AI|August 1, 2025
FHIR-GPT Enhances Health Interoperability with Large Language ModelsYikuan Li, Hanyin Wang, Halid Z Yerebakan, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|December 3, 2025
Fast Multi-Organ Fine Segmentation in CT Images with Hierarchical Sparse Sampling and Residual TransformerXueqi Guo, Halid Ziya Yerebakan, Yoshihisa Shinagawa, et al.
IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society|April 19, 2006
Automatic 3-D grayscale volume matching and shape analysisGrégoire Guétat, Matthieu Maitre, Laurène Joly, et al.
Information Processing in Medical Imaging : Proceedings of the ... Conference|July 30, 2015
Bodypart Recognition Using Multi-stage Deep LearningZhennan Yan, Yiqiang Zhan, Zhigang Peng, et al.
IEEE Transactions on Medical Imaging|February 11, 2016
Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart RecognitionZhennan Yan, Yiqiang Zhan, Zhigang Peng, et al.
Academic Radiology|May 20, 2023
Reduction in Radiologist Interpretation Time of Serial CT and MR Imaging Findings with Deep Learning Identification of Relevant Priors, Series and Finding LocationsThomas Weikert, Harold I Litt, William H Moore, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
IEEE Transactions on Pattern Analysis and Machine Intelligence|September 13, 2008
Homotopic image pseudo-invariants for openset object recognition and image retrievalYoshihisa Shinagawa
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|May 17, 2008
Wavelet-based joint estimation and encoding of depth-image-based representations for free-viewpoint renderingMatthieu Maitre, Yoshihisa Shinagawa, Minh N Do
IEEE Transactions on Bio-Medical Engineering|September 30, 2005
Simulating pathological gait using the enhanced linear inverted pendulum modelTaku Komura, Akinori Nagano, Howard Leung, et al.
NEJM AI|August 1, 2025
FHIR-GPT Enhances Health Interoperability with Large Language ModelsYikuan Li, Hanyin Wang, Halid Z Yerebakan, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|December 3, 2025
Fast Multi-Organ Fine Segmentation in CT Images with Hierarchical Sparse Sampling and Residual TransformerXueqi Guo, Halid Ziya Yerebakan, Yoshihisa Shinagawa, et al.
IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society|April 19, 2006
Automatic 3-D grayscale volume matching and shape analysisGrégoire Guétat, Matthieu Maitre, Laurène Joly, et al.
Information Processing in Medical Imaging : Proceedings of the ... Conference|July 30, 2015
Bodypart Recognition Using Multi-stage Deep LearningZhennan Yan, Yiqiang Zhan, Zhigang Peng, et al.
IEEE Transactions on Medical Imaging|February 11, 2016
Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart RecognitionZhennan Yan, Yiqiang Zhan, Zhigang Peng, et al.
Academic Radiology|May 20, 2023
Reduction in Radiologist Interpretation Time of Serial CT and MR Imaging Findings with Deep Learning Identification of Relevant Priors, Series and Finding LocationsThomas Weikert, Harold I Litt, William H Moore, et al.
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