須鎗弘樹

研究者氏名	須鎗弘樹
	スヤリヒロキ
URL	https://sites.google.com/view/hirokisuyari/
所属	千葉大学
部署	大学院工学研究院総合工学講座
職名	教授
学位	理学士(東京理科大学), 理学修士(東京理科大学), 博士(理学)(東京理科大学)
J-Global ID	200901046263265090

研究キーワード

エントロピー ,複雑系 ,情報理論

研究分野

情報通信 / 情報学基礎論 /

経歴

2013年4月

現在

千葉大学工学部教授

2008年4月

2013年3月

千葉大学工学部情報画像学科准教授

2000年4月

2008年3月

千葉大学工学部情報画像工学科助教授

論文

Multidimensional Deep Learning Reduces False-Positives in the Automated Detection of Cerebral Aneurysms on Time-Of-Flight Magnetic Resonance Angiography: A Multi-Center Study

Yuki Terasaki Hajime Yokota Kohei Tashiro Takuma Maejima Takashi Takeuchi Ryuna Kurosawa Shoma Yamauchi Akiyo Takada Hiroki Mukai Kenji Ohira Joji Ota Takuro Horikoshi Yasukuni Mori Takashi Uno Hiroki Suyari

Frontiers in Neurology 12 742126-742126 2022年1月 [査読有り]

Current deep learning-based cerebral aneurysm detection demonstrates high sensitivity, but produces numerous false-positives (FPs), which hampers clinical application of automated detection systems for time-of-flight magnetic resonance angiography...

Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer

Yasukuni Mori Hajime Yokota Isamu Hoshino Yosuke Iwatate Kohei Wakamatsu Takashi Uno Hiroki Suyari

Scientific Reports 11(1) 16521-16521 2021年12月 [査読有り]

<title>Abstract</title>The selection of genes that are important for obtaining gene expression data is challenging. Here, we developed a deep learning-based feature selection method suitable for gene selection. Our novel deep learning model includ...

Development of attenuation correction methods using deep learning in brain‐perfusion single‐photon emission computed tomography

Taisuke Murata Hajime Yokota Ryuhei Yamato Takuro Horikoshi Masato Tsuneda Ryuna Kurosawa Takuma Hashimoto Joji Ota Koichi Sawada Takashi Iimori Yoshitada Masuda Yasukuni Mori Hiroki Suyari Takashi Uno

Medical Physics 48(8) 4177-4190 2021年8月 [査読有り]

PURPOSE: Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation co...

A Deep Convolutional Neural Network With Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma

Satoshi Maki Takeo Furuya Takuro Horikoshi Hajime Yokota Yasukuni Mori Joji Ota Yohei Kawasaki Takuya Miyamoto Masaki Norimoto Sho Okimatsu Yasuhiro Shiga Kazuhide Inage Sumihisa Orita Hiroshi Takahashi Hiroki Suyari Takashi Uno Seiji Ohtori

Spine 45(10) 694-700 2020年5月 [査読有り]

STUDY DESIGN: Retrospective analysis of magnetic resonance imaging (MRI). OBJECTIVE: The aim of this study was to evaluate the performance of our convolutional neural network (CNN) in differentiating between spinal schwannoma and meningioma on MRI...

Advantages of q-logarithm representation over q-exponential representation from the sense of scale and shift on nonlinear systems

Hiroki Suyari Hiroshi Matsuzoe Antonio M. Scarfone

The European Physical Journal Special Topics 229(5) 773-785 2020年3月 [査読有り]

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