Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences : MRMS)
Source: Magnetic Resonance in Medical Sciences : MRMS - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences)
Source: Magnetic Resonance in Medical Sciences - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences : MRMS)
Source: Magnetic Resonance in Medical Sciences : MRMS - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences : MRMS)
Source: Magnetic Resonance in Medical Sciences : MRMS - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences)
Source: Magnetic Resonance in Medical Sciences - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences : MRMS)
Source: Magnetic Resonance in Medical Sciences : MRMS - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences)
Source: Magnetic Resonance in Medical Sciences - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences : MRMS)
Source: Magnetic Resonance in Medical Sciences : MRMS - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences)
Source: Magnetic Resonance in Medical Sciences - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences : MRMS)
Source: Magnetic Resonance in Medical Sciences : MRMS - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences)
Source: Magnetic Resonance in Medical Sciences - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences : MRMS)
Source: Magnetic Resonance in Medical Sciences : MRMS - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning
CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.PMID:34897147 | DOI:10.2463/mrms.mp.2021-0068 (Source: Magnetic Resonance in Medical Sciences)
Source: Magnetic Resonance in Medical Sciences - December 13, 2021 Category: Radiology Authors: Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki A Source Type: research
