Koji Fujimoto

特定准教授 / Program-Specific Associate Professor

Kyoto University

Japan

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About

Koji Fujimoto is a Program-Specific Associate Professor at Kyoto University in Japan. His research primarily focuses on the application of advanced imaging techniques in clinical settings, particularly magnetic resonance imaging (MRI) and its implications for placental function and cancer assessment. Additionally, he explores the utilization of artificial intelligence, including natural language processing, to enhance radiology report summarization and diagnostic accuracy.

Articles (15)

Year:

2025

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2021

Year:

2020

Collaborators (10)

Tomohisa Okada

准教授 / Associate Professor

Kyoto University

JAPAN

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Ryosuke Takahashi

教授 / Professor

Kyoto University

JAPAN

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Nobukatsu Sawamoto

JAPAN

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Aki Kido

Associate Professor

University of Toyama

JAPAN

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Yuji Nakamoto

Kyoto University

JAPAN

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Sachiko Minamiguchi

准教授 / Associate Professor

Kyoto University

JAPAN

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Yuki Himoto

助教 / Assistant Professor

Kyoto University

JAPAN

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Yasutaka Fushimi

Kyoto Prefectural University of Medicine

JAPAN

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Hidetoshi Matsuo

Specially Appointed Assistant Professor

Kobe University

JAPAN

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Martijn Cloos

University of Queensland

AUSTRALIA

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Koji Fujimoto

Research Interests

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About

Articles (15)

Toward Clinical Implementation of Magnetic Resonance Imaging for Placental Function

Fully automatic summarization of radiology reports using natural language processing with large language models

Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists' decisions of deep myometrial invasion (vol 85, pg 161, 2022)

Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer

GABA, glutamate and excitatory-inhibitory ratios measured using short-TE STEAM MRS at 7-Tesla: Effects of macromolecule basis sets and baseline parameters

Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance

Bleeding alert map (BAM): The identification method of the bleeding source in real organs using datasets made on mimicking organs

Bleeding alert map (BAM): The identification method of the bleeding source in real organs using datasets made on mimicking organs

Corrigendum to “Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists' decisions of deep myometrial invasion” [Magnetic Resonance Imaging 85 (2022) 161–167]., (S0730725X21001910), (10.1016/j.mri.2021.10.024)

Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance

GABA, glutamate and excitatory-inhibitory ratios measured using short-TE STEAM MRS at 7-Tesla: Effects of macromolecule basis sets and baseline parameters

Insertable inductively coupled volumetric coils for MR microscopy in a human 7T MR system

Super-resolution application of generative adversarial network on brain time-of-flight MR angiography: image quality and diagnostic utility evaluation

Free‐breathing abdominal T1 mapping using an optimized MR fingerprinting sequence

Attribute-guided image generation of three-dimensional computed tomography images of lung nodules using a generative adversarial network

Collaborators (10)

Tomohisa Okada

Ryosuke Takahashi

Nobukatsu Sawamoto

Aki Kido

Yuji Nakamoto

Sachiko Minamiguchi

Yuki Himoto

Yasutaka Fushimi

Hidetoshi Matsuo

Martijn Cloos

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