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Akitoshi Shimazaki

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Japanese Journal of Radiology|December 4, 2018
Technical and clinical overview of deep learning in radiologyDaiju Ueda, Akitoshi Shimazaki, Yukio Miki
Scientific Reports|January 15, 2022
Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation methodAkitoshi Shimazaki, Daiju Ueda, Antoine Choppin, et al.
JCO Precision Oncology|January 7, 2022
Training, Validation, and Test of Deep Learning Models for Classification of Receptor Expressions in Breast Cancers From MammogramsDaiju Ueda, Akira Yamamoto, Tsutomu Takashima, et al.
Radiology. Artificial Intelligence|April 8, 2022
Development and Validation of Artificial Intelligence-based Method for Diagnosis of Mitral Regurgitation from Chest RadiographsDaiju Ueda, Shoichi Ehara, Akira Yamamoto, et al.
European Heart Journal. Digital Health|January 30, 2023
Artificial intelligence-based detection of aortic stenosis from chest radiographsDaiju Ueda, Akira Yamamoto, Shoichi Ehara, et al.
Radiology|October 24, 2018
Deep Learning for MR Angiography: Automated Detection of Cerebral AneurysmsDaiju Ueda, Akira Yamamoto, Masataka Nishimori, et al.
European Journal of Radiology|July 14, 2022
Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolutionTakashi Honjo, Daiju Ueda, Yutaka Katayama, et al.
Japanese Journal of Radiology|November 17, 2020
Visualizing "featureless" regions on mammograms classified as invasive ductal carcinomas by a deep learning algorithm: the promise of AI support in radiologyDaiju Ueda, Akira Yamamoto, Tsutomu Takashima, et al.
BMC Cancer|October 19, 2021
Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation studyDaiju Ueda, Akira Yamamoto, Akitoshi Shimazaki, et al.
Radiology|March 31, 2021
Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration ArtifactsDaiju Ueda, Yutaka Katayama, Akira Yamamoto, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Japanese Journal of Radiology|December 4, 2018
Technical and clinical overview of deep learning in radiologyDaiju Ueda, Akitoshi Shimazaki, Yukio Miki
Scientific Reports|January 15, 2022
Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation methodAkitoshi Shimazaki, Daiju Ueda, Antoine Choppin, et al.
JCO Precision Oncology|January 7, 2022
Training, Validation, and Test of Deep Learning Models for Classification of Receptor Expressions in Breast Cancers From MammogramsDaiju Ueda, Akira Yamamoto, Tsutomu Takashima, et al.
Radiology. Artificial Intelligence|April 8, 2022
Development and Validation of Artificial Intelligence-based Method for Diagnosis of Mitral Regurgitation from Chest RadiographsDaiju Ueda, Shoichi Ehara, Akira Yamamoto, et al.
European Heart Journal. Digital Health|January 30, 2023
Artificial intelligence-based detection of aortic stenosis from chest radiographsDaiju Ueda, Akira Yamamoto, Shoichi Ehara, et al.
Radiology|October 24, 2018
Deep Learning for MR Angiography: Automated Detection of Cerebral AneurysmsDaiju Ueda, Akira Yamamoto, Masataka Nishimori, et al.
European Journal of Radiology|July 14, 2022
Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolutionTakashi Honjo, Daiju Ueda, Yutaka Katayama, et al.
Japanese Journal of Radiology|November 17, 2020
Visualizing "featureless" regions on mammograms classified as invasive ductal carcinomas by a deep learning algorithm: the promise of AI support in radiologyDaiju Ueda, Akira Yamamoto, Tsutomu Takashima, et al.
BMC Cancer|October 19, 2021
Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation studyDaiju Ueda, Akira Yamamoto, Akitoshi Shimazaki, et al.
Radiology|March 31, 2021
Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration ArtifactsDaiju Ueda, Yutaka Katayama, Akira Yamamoto, et al.
Pageof 2