野村行弘

研究者氏名	野村行弘
	ノムラユキヒロ
URL
所属	千葉大学
部署	フロンティア医工学センター
職名	准教授
学位	博士（工学）(千葉大学)
その他の所属	東京大学医学部附属病院コンピュータ画像診断学／予防医学講座
J-Global ID	201901001849008767

研究分野

ライフサイエンス / 医用システム /

経歴

2021年7月

現在

千葉大学フロンティア医工学センター准教授

2019年5月

2021年6月

東京大学医学部附属病院特任講師

2006年4月

2019年4月

東京大学医学部附属病院特任研究員

受賞

2020年5月

日本医学放射線学会, JJR優秀論文賞
野村行弘

2015年11月

日本医用画像工学会, MIT誌論文賞,遠隔読影環境における多施設連携型CAD開発、実運用、および継続的性能改善
野村行弘増谷佳孝三木聡一郎花岡昇平根本充貴吉川健啓林直人大友邦

論文

12 3 4 5 >

Performance changes due to differences among annotating radiologists for training data in computerized lesion detection

Yukihiro Nomura Shouhei Hanaoka Naoto Hayashi Takeharu Yoshikawa Saori Koshino Chiaki Sato Momoko Tatsuta Yuya Tanaka Shintaro Kano Moto Nakaya Shohei Inui Masashi Kusakabe Takahiro Nakao Soichiro Miki Takeyuki Watadani Ryusuke Nakaoka Akinobu Shimizu Osamu Abe

International Journal of Computer Assisted Radiology and Surgery 2024年4月 [査読有り]

Capability of GPT-4V(ision) in the Japanese National Medical Licensing Examination: Evaluation Study.

Takahiro Nakao Soichiro Miki Yuta Nakamura Tomohiro Kikuchi Yukihiro Nomura Shouhei Hanaoka Takeharu Yoshikawa Osamu Abe

JMIR medical education 10 e54393 2024年3月 [査読有り]

Abstract

Background

Previous research applying large language models (LLMs) to medicine was focused on text-based information. Recently, multimodal variants of LLMs acquired the capability of recognizing images.

Objective

To evaluate the capab...

Breath Measurement Method for Synchronized Reproduction of Biological Tones in an Augmented Reality Auscultation Training System

Yukiko Kono Keiichiro Miura Hajime Kasai Shoichi Ito Mayumi Asahina Masahiro Tanabe Yukihiro Nomura Toshiya Nakaguchi

Sensors 24(5) 1626-1626 2024年3月

An educational augmented reality auscultation system (EARS) is proposed to enhance the reality of auscultation training using a simulated patient. The conventional EARS cannot accurately reproduce breath sounds according to the breathing of a simu...

Development and evaluation of an integrated liver nodule diagnostic method by combining the liver segment division and lesion localization/classification models for enhanced focal liver lesion detection.

Tomomi Takenaga Shouhei Hanaoka Yukihiro Nomura Takahiro Nakao Hisaichi Shibata Soichiro Miki Takeharu Yoshikawa Naoto Hayashi Osamu Abe

Radiological physics and technology 17(1) 103-111 2024年3月 [査読有り]

The purpose of the study was to develop a liver nodule diagnostic method that accurately localizes and classifies focal liver lesions and identifies the specific liver segments in which they reside by integrating a liver segment division algorithm...

Improved identification of tumors in 18F-FDG-PET examination by normalizing the standard uptake in the liver based on blood test data.

Md Ashraful Alam Shouhei Hanaoka Yukihiro Nomura Tomohiro Kikuchi Takahiro Nakao Tomomi Takenaga Naoto Hayashi Takeharu Yoshikawa Osamu Abe

International journal of computer assisted radiology and surgery 19(3) 581-590 2024年3月 [査読有り]

PURPOSE: Standardized uptake values (SUVs) derived from 18F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography are a crucial parameter for identifying tumors or abnormalities in an organ. Moreover, exploring ways to improve ...

MISC

<12

【Step up MRI 2015-臨床応用のためのMRI技術の現在と未来-】臨床応用に向けた技術開発の現状と展望大量の臨床画像データの活用における問題 4万例を超える臨床画像データの処理を行ったCADシステムの開発・運用経験から

増谷佳孝野村行弘三木総一郎根本充貴花岡昇平吉川健啓林直人

INNERVISION 30(9) 35-37 2015年8月

Clinical machine learning in action: CAD system design, development, tuning, and long-term experience

Yoshitaka Masutani Mitsutaka Nemoto Yukihiro Nomura Naoto Hayashi

Image Processing: Concepts, Methodologies, Tools, and Applications 2-3 621-638 2013年5月 [査読有り]

A machine learning-based method using a database of clinical data, such as Computer-Assisted Detection/Diagnosis (CAD), is one of the next key technologies in clinical imaging. The most important issue for machine learning, based on clinical data,...

【Step up MRI2002】循環器領域におけるMRIの最新動向循環器領域のMRIに必要な解剖・生理機能のポイント

藤本進一郎南條修二山崎純一吉川宏起井上優介野村行弘

INNERVISION 17(9) 1-4 2002年8月