GUANG LI 李 広

李 広 Guang Li
Guang Li received the B.S. and B.A. degrees with honors in Software Engineering (Japanese Intensive) from Dalian University of Technology, China, in 2019, and the M.S. degree from the Graduate School of Information Science and Technology, Hokkaido University, Japan, in 2022. He is currently pursuing his Ph.D. degree from the Graduate School of Information Science and Technology, Hokkaido University, Japan. He is a student member of AAAI, IEEE, IEICE, and CCF. His research interests include machine learning, image processing, and medical image analysis. He was a recipient of the Best Student Paper Award in IEEE GCCE 2020 and the Top 3% Impact Paper in IEEE ICIP 2020. His papers have been featured in Scientific American, Nikkei, etc. He has served as a reviewer for IEEE Transactions on Medical Imaging, Pattern Recognition, Applied Soft Computing, Information Sciences, etc.

E-mail: guang[at]lmd.ist.hokudai.ac.jp

Google Scholar ResearchGate LinkedIn Facebook Twitter GitHub

経歴 研究業績 受賞 研究発表 その他の雑誌・報道発表など 外部資金獲得 学会活動 記念 訪問者

 

経歴

学歴

  • 2022年4月-現在 北海道大学大学院情報科学院 博士課程
  • 2020年4月-2022年3月 北海道大学大学院情報科学院 修士課程
  • 2015年9月-2019年6月 大連理工大学ソフトウェア工学部 ソフトウェア工学 (日本語インテンシブ)

職歴

  • 2022年7月-現在 文部科学省データ関連人材育成プログラム リサーチアシスタント
  • 2022年6月-現在 日本無線株式会社 連携研究者
  • 2022年4月-現在 北大・日立協働教育研究支援プログラム リサーチフェロー
  • 2020年6月-現在 オリンパス株式会社 連携研究者
  • 2019年7月-2019年9月 中国科学院自動化研究所 Brainnetome Center リサーチインターン (Advisor: Prof. Shan Yu)
  • 2018年5月-2019年6月 大連理工大学 DUT Media Lab リサーチインターン (Advisor: Prof. Haojie Li)

 

研究業績

プロジェクト

  1. Guang Li, Bo Zhao, Tongzhou Wang, “Awesome-Dataset-Distillation,” 2022. (Collaborator: Bo Zhao and Tongzhou Wang) [Dataset Distillation] [Awesome-Dataset-Distillation]

論文誌

  1. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “COVID-19 detection via self-supervised learning and batch knowledge ensembling using chest X-ray images,” 2022. (Under review)
  2. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “COVID-19 detection based on self-supervised transfer learning using chest X-ray images,” 2022. (Under review)
  3. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-supervised learning for gastritis detection with gastric X-ray images,” 2022. (Under review) [arXiv]
  4. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Compressed gastric image generation based on soft-label dataset distillation for medical data sharing,” Computer Methods and Programs in Biomedicine (CMPB), Elsevier, pp. 1-11, 2022. (IF: 7.027, Minor revision) [arXiv]
  5. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset complexity assessment based on cumulative maximum scaled area under Laplacian spectrum,” Multimedia Tools and Applications (MTAP), Springer, vol. 81, no. 22, pp. 32287-32303, 2022. (IF: 2.577) [arXiv] [Paper]

国際会議

  1. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation using parameter pruning,” 2022. (Under review) [arXiv]
  2. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation for medical dataset sharing,” 2022. (Under review) [arXiv]
  3. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-knowledge distillation based self-supervised learning for COVID-19 detection from chest X-ray images,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1371-1375, Singapore, 2022. [arXiv] [Paper] [Link]
  4. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “TriBYOL: Triplet BYOL for self-supervised representation learning,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3458-3462, Singapore, 2022. [arXiv] [Paper] [Link]
  5. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-supervised transfer learning for COVID-19 detection from chest X-ray images,” AAAI Conference on Artificial Intelligence (AAAI), Workshop, pp. 1-6, Vancouver, Canada, 2022. [Paper]
  6. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Triplet self-supervised learning for gastritis detection with scarce annotations,” IEEE Global Conference on Consumer Electronics (GCCE), pp. 787-788, Kyoto, Japan, 2021. [Paper]
  7. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Cross-view self-supervised learning via momentum statistics in batch normalization,” IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 1-2, Penghu, Taiwan, 2021. [Paper]
  8. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Complexity evaluation of medical image data for classification problem based on spectral clustering,” IEEE Global Conference on Consumer Electronics (GCCE), pp. 667-669, Kobe, Japan, 2020. (IEEE GCCE2020 Excellent Student Paper Award Gold Prize) [Paper]
  9. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Soft-label anonymous gastric X-ray image distillation,” IEEE International Conference on Image Processing (ICIP), pp. 305-309, Abu Dhabi, UAE, 2020. (The first paper to explore medical dataset distillation; IEEE ICIP 2020 Top 3% Impact Paper; Featured in SCIENTIFIC AMERICAN, NIKKEI, etc.; Cited by ICLR 2021 Oral (The Second Highest Score Paper), CVPR 2022 Oral (CMU, MIT, UC Berkeley), ICML 2022 Oral (Outstanding Paper Award), NeurIPS 2022, etc.) [arXiv] [Paper] [Link]

国内会議

  1. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “COVID-19 detection based on masked image modeling using vision transformer,” Meeting on Image Recognition and Understanding (MIRU), pp. 1-4, Himeji, 2022.
  2. Guang Li, Ren Togo, Katsuhiro Mabe, Shunpei Nishida, Yoshihiro Tomoda, Takahiro Ogawa, Miki Haseyama, “A note on automatic diagnosis of Helicobacter pylori infection based on self-supervised learning and self-knowledge distillation,” ITE Technical Report, vol. 46, no. 6, pp. 49-52, Sapporo, 2022.
  3. Guang Li, Ren Togo, Katsuhiro Mabe, Shunpei Nishida, Yoshihiro Tomoda, Hikari Shimizu, Takahiro Ogawa, Miki Haseyama, “A note on automatic diagnosis of Helicobacter pylori infection based on EfficientNet with flooding loss,” ITE Technical Report, vol. 45, no. 4, pp. 23-26, Sapporo, 2021.
  4. 李 広, 藤後 廉, 小川 貴弘, 長谷山 美紀, “nAULSに基づくデータセットの複雑性評価に関する検討,” 電気・情報関係学会北海道支部連合大会 講演論文集, pp. 98-99, 札幌, 2020.

 

受賞

  1. IEICE Hokkaido Chapter Student Encouragement Award (2022) [Link]
  2. IEEE ICIP 2020 Top 3% Impact Paper (Guang Li et al., 2022) [Link]
  3. JGC-S Scholarship (2021)
  4. IEEE GCCE 2020 Excellent Student Paper Award Gold Prize (Guang Li et al., 2020) [Link]
  5. Hokkaido University-Hitachi Collaborative Education and Research Program (Fellowship, 2020) [News] [News]
  6. Outstanding Graduate of Dalian University of Technology (2019)

 

研究発表

講演

  1. “AI技術の最新動向と医療分野における応用事例,” 第1回 北海道大学医療AIシンポジウム, 藤後 廉, 2022/11/05. (Guang Li et al., ICIP 2020; ICASSP 2022) [Link]
  2. “異分野連携によるデータ駆動型の AI 研究に関する取り組み,” 第129回 日本画像学会年次大会, 小川 貴弘, 2022/06/24. (Guang Li et al., ICIP 2020; ICASSP 2022) [Link]
  3. “AIの社会実装を加速するデータ駆動型研究と人材育成,” Clinical AI Human Resources Development Program 2nd アニュアルシンポジウム, 長谷山 美紀, 2022/02/15. (Guang Li et al., ICIP 2020; ICASSP 2022) [Link]
  4. “マルチメディアAI技術に基づく異分野融合研究と実社会応用,” 映像情報メディア学会 メディア工学研究会, 小川 貴弘, 2021/06/04. (Guang Li et al., ICIP 2020) [Link]
  5. “AIを中心とした医療デジタル技術基盤の構築へ向けた取り組み,” 第60回日本消化器がん検診学会総会 附置研究会3, 藤後 廉, 2021/06/04. (Guang Li et al., ICIP 2020) [Link]

ポスター

  1. “医療データを対象としたデータセット蒸留に関する検討,” 第1回 北海道大学医療AIシンポジウム, 李 広, 2022/11/05. [Link]
  2. “最先端マルチメディアAI技術で人間の知識獲得プロセスを探る,” 北楡会・北海道大学情報系交流会, 小川 直輝・李 広, 2022/09/23. [Link]

 

その他の雑誌・報道発表など

  1. “Most Popular AI Research Aug 2022,” LibHunt, 2022/09/03. (Guang Li et al., Awesome-Dataset-Distillation) [News]
  2. “最先端マルチメディア技術とその実社会応用,” 北海道総合通信局公式チャンネル, 2022/04/20. (Guang Li et al., ICIP 2020; ICASSP 2022) [News]
  3. “肺X線画像からのCOVID-19肺炎検出AIの構築,” 北海道大学病院 医療AI研究開発センター プロジェクト紹介, 2022/03/29. (Guang Li et al., ICASSP 2022) [News]
  4. “医用画像データ共有へ向けたデータ圧縮技術の構築,” 北海道大学病院 医療AI研究開発センター プロジェクト紹介, 2022/03/29. (Guang Li et al., ICIP 2020) [News]
  5. “メディア工学の研究動向,” 映像情報メディア年報2021-22シリーズ, 2022/01/01. (Guang Li et al., ICIP 2020) [News]
  6. “北海道大 情報科学研究院 AI活用でインフラ点検,” 日本経済新聞, 2021/06/16. (Guang Li et al., ICIP 2020) [News]
  7. “如何让人工智能技术更亲民,” 环球科学, 2021/01/08. (Guang Li et al., ICIP 2020) [News]
  8. “How to Make Artificial Intelligence More Democratic,” DECCAN HERALD, 2021/01/02. (Guang Li et al., ICIP 2020) [News]
  9. “How to Make Artificial Intelligence More Democratic,” SCIENTIFIC AMERICAN, 2021/01/02. (Guang Li et al., ICIP 2020) [News]

 

外部資金獲得

  1. 北大・日立協働教育研究支援プログラム, “AI 医療を加速するデータマイニングに関する研究” (2022/04~) (研究代表者)

 

学会活動

査読経験

  1. IEEE Transactions on Medical Imaging (TMI) [Link]
  2. Pattern Recognition (PR) [Link]
  3. Applied Soft Computing (ASOC) [Link]
  4. Information Sciences (INS) [Link]
  5. Computers in Biology and Medicine (CBM) [Link]
  6. Digital Signal Processing (DSP) [Link]
  7. The 25th Meeting on Image Recognition and Understanding (MIRU2022) [Link]

 

記念

 

訪問者

  unique visitors since 2022/02/13