裕二 池谷

3.2k total citations · 1 hit paper
19 papers, 1.3k citations indexed

About

裕二 池谷 is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, 裕二 池谷 has authored 19 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 9 papers in Molecular Biology and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in 裕二 池谷's work include Topic Modeling (10 papers), Biomedical Text Mining and Ontologies (8 papers) and Machine Learning in Healthcare (5 papers) 裕二 池谷 is often cited by papers focused on Topic Modeling (10 papers), Biomedical Text Mining and Ontologies (8 papers) and Machine Learning in Healthcare (5 papers) 裕二 池谷 collaborates with scholars based in United States, China and Canada 裕二 池谷's co-authors include Tristan Naumann, Hoifung Poon, Naoto Usuyama, Robert Tinn, Jianfeng Gao, Hao Cheng, Michael Lucas, Xiaodong Liu, Yuan Gao and Yang Huang and has published in prestigious journals such as Nature Methods, Journal of Materials Chemistry A and Sensors and Actuators B Chemical.

In The Last Decade

裕二 池谷

16 papers receiving 1.3k citations

Hit Papers

Domain-Specific Language ... 2021 2026 2022 2024 2021 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
    2021 1.0k citations 裕二 池谷, Robert Tinn et al. arXiv (Cornell University) profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
裕二 池谷 United States 10 948 534 175 122 90 19 1.3k
Naoto Usuyama United States 7 867 0.9× 500 0.9× 175 1.0× 124 1.0× 88 1.0× 14 1.2k
Robert Tinn United States 4 840 0.9× 483 0.9× 166 0.9× 105 0.9× 71 0.8× 5 1.1k
Michael Lucas Australia 4 790 0.8× 456 0.9× 146 0.8× 91 0.7× 68 0.8× 14 1.1k
Qiao Jin United States 16 647 0.7× 273 0.5× 328 1.9× 131 1.1× 78 0.9× 51 1.2k
Qiang Wei China 13 470 0.5× 374 0.7× 87 0.5× 137 1.1× 34 0.4× 40 1.1k
Oya Beyan Germany 17 406 0.4× 203 0.4× 117 0.7× 227 1.9× 75 0.8× 64 999
Asma Ben Abacha United States 20 1.0k 1.1× 418 0.8× 98 0.6× 69 0.6× 257 2.9× 51 1.3k
Honghan Wu United Kingdom 18 528 0.6× 263 0.5× 104 0.6× 87 0.7× 51 0.6× 77 1.1k
Renqian Luo China 7 408 0.4× 157 0.3× 144 0.8× 72 0.6× 58 0.6× 10 799
Majid Rastegar-Mojarad United States 18 899 0.9× 796 1.5× 80 0.5× 54 0.4× 34 0.4× 48 1.5k

Countries citing papers authored by 裕二 池谷

Since Specialization
Citations

This map shows the geographic impact of 裕二 池谷's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by 裕二 池谷 with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites 裕二 池谷 more than expected).

Fields of papers citing papers by 裕二 池谷

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by 裕二 池谷. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by 裕二 池谷. The network helps show where 裕二 池谷 may publish in the future.

Co-authorship network of co-authors of 裕二 池谷

This figure shows the co-authorship network connecting the top 25 collaborators of 裕二 池谷. A scholar is included among the top collaborators of 裕二 池谷 based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with 裕二 池谷. 裕二 池谷 is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Liu, Liang, et al.. (2025). Segmentation of PET/CT lung cancer lesion images via a semi-supervised improved SwinUNet model. Biomedical Signal Processing and Control. 113. 108797–108797.
2.
Wu, Qianhui, et al.. (2025). Magma: A Foundation Model for Multimodal AI Agents. 14203–14214. 4 indexed citations
3.
池谷, 裕二, et al.. (2024). Brain tumor image segmentation based on Semantic Flow Guided Sampling and Attention Mechanism. Optics and Precision Engineering. 32(4). 565–577. 1 indexed citations
4.
Zhang, Jie, et al.. (2024). Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech Representation. Proceedings of the AAAI Conference on Artificial Intelligence. 38(17). 19768–19776. 3 indexed citations
5.
池谷, 裕二, Jianwei Yang, Naoto Usuyama, et al.. (2024). A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities. Nature Methods. 22(1). 166–176. 31 indexed citations
6.
Tinn, Robert, Hao Cheng, 裕二 池谷, et al.. (2023). Fine-tuning large neural language models for biomedical natural language processing. Patterns. 4(4). 100729–100729. 72 indexed citations
7.
Yu, Donghan, 裕二 池谷, Chenyan Xiong, & Yiming Yang. (2023). CompleQA: Benchmarking the Impacts of Knowledge Graph Completion Methods on Question Answering. 12748–12755.
8.
Zhang, Kai, Yue Huang, 裕二 池谷, Fan Yang, & Nan Hao. (2023). A novel isothermal amplification strategy for rapid and sensitive detection of Matrix Metalloproteinase 2 using a bipedal DNA walker in anti-aging research. Sensors and Actuators B Chemical. 397. 134650–134650. 6 indexed citations
9.
池谷, 裕二, Xiang Deng, & Yu Su. (2023). Don’t Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments. 4928–4949. 17 indexed citations
10.
Mu, Wei, Rajesh C. Rao, Robert Tinn, et al.. (2023). Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision. Patterns. 4(4). 100726–100726. 15 indexed citations
11.
Li, Tianle, et al.. (2023). Few-shot In-context Learning on Knowledge Base Question Answering. 6966–6980. 15 indexed citations
12.
Li, Xinze, Zhenghao Liu, Chenyan Xiong, et al.. (2023). Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data. 11560–11574. 3 indexed citations
13.
Zhang, Xiaoyue, Chaoran Dong, Yong Yang, et al.. (2023). Highly selective photothermal conversion of CO2 to ethylene using hierarchical boxwood ball-like Weyl semimetal WTe2 catalysts. Journal of Materials Chemistry A. 12(2). 923–931. 4 indexed citations
14.
池谷, 裕二, Robert Tinn, Hao Cheng, et al.. (2021). Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing. arXiv (Cornell University). 3(1). 1–23. 1027 indexed citations breakdown →
15.
Chen, Long, 裕二 池谷, Zhiyong Sun, et al.. (2020). Clinical concept normalization with a hybrid natural language processing system combining multilevel matching and machine learning ranking. Journal of the American Medical Informatics Association. 27(10). 1576–1584. 10 indexed citations
16.
Chen, Long, 裕二 池谷, Xin Ji, et al.. (2019). Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning. Journal of the American Medical Informatics Association. 27(1). 56–64. 44 indexed citations
17.
Chen, Long, 裕二 池谷, Xin Ji, et al.. (2019). Clinical trial cohort selection based on multi-level rule-based natural language processing system. Journal of the American Medical Informatics Association. 26(11). 1218–1226. 30 indexed citations
18.
Liu, Yang, et al.. (2017). Symptom severity classification with gradient tree boosting. Journal of Biomedical Informatics. 75. S105–S111. 24 indexed citations
19.
Li, Dingcheng, Ming Huang, 裕二 池谷, et al.. (2017). Mapping client messages to a unified data model with mixture feature embedding convolutional neural network. 2. 386–391. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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