Naoto Usuyama

3.8k total citations · 1 hit paper
14 papers, 1.2k citations indexed

About

Naoto Usuyama is a scholar working on Artificial Intelligence, Molecular Biology and Health Informatics. According to data from OpenAlex, Naoto Usuyama has authored 14 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 5 papers in Molecular Biology and 3 papers in Health Informatics. Recurrent topics in Naoto Usuyama's work include Topic Modeling (6 papers), Biomedical Text Mining and Ontologies (4 papers) and Machine Learning in Healthcare (3 papers). Naoto Usuyama is often cited by papers focused on Topic Modeling (6 papers), Biomedical Text Mining and Ontologies (4 papers) and Machine Learning in Healthcare (3 papers). Naoto Usuyama collaborates with scholars based in United States, United Kingdom and Japan. Naoto Usuyama's co-authors include Tristan Naumann, Hoifung Poon, 裕二池谷, Robert Tinn, Jianfeng Gao, Hao Cheng, Michael Lucas, Xiaodong Liu, Xiaodong Liu and Amanda K. Hall and has published in prestigious journals such as Bioinformatics, Nature Methods and Annals of Oncology.

In The Last Decade

Naoto Usuyama

12 papers receiving 1.2k citations

Hit Papers

1 papers align trajectories

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.

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						Papers	Cites
Naoto Usuyama United States	7	867	500	175	124	88	14	1.2k
裕二池谷 United States	10	948 1.1×	534 1.1×	175 1.0×	122 1.0×	90 1.0×	19	1.3k
Robert Tinn United States	4	840 1.0×	483 1.0×	166 0.9×	105 0.8×	71 0.8×	5	1.1k
Michael Lucas Australia	4	790 0.9×	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
Honghan Wu United Kingdom	18	528 0.6×	263 0.5×	104 0.6×	87 0.7×	51 0.6×	77	1.1k
Aurélie Névéol France	19	944 1.1×	809 1.6×	75 0.4×	71 0.6×	27 0.3×	71	1.4k
Renqian Luo China	7	408 0.5×	157 0.3×	144 0.8×	72 0.6×	58 0.7×	10	799
Majid Rastegar-Mojarad United States	18	899 1.0×	796 1.6×	80 0.5×	54 0.4×	34 0.4×	48	1.5k
William Boag United States	6	715 0.8×	301 0.6×	99 0.6×	83 0.7×	52 0.6×	12	873
Oya Beyan Germany	17	406 0.5×	203 0.4×	117 0.7×	227 1.8×	75 0.9×	64	999

Countries citing papers authored by Naoto Usuyama

Since Specialization

Citations

This map shows the geographic impact of Naoto Usuyama'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 Naoto Usuyama with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Naoto Usuyama more than expected).

Fields of papers citing papers by Naoto Usuyama

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Naoto Usuyama. 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 Naoto Usuyama. The network helps show where Naoto Usuyama may publish in the future.

Co-authorship network of co-authors of Naoto Usuyama

This figure shows the co-authorship network connecting the top 25 collaborators of Naoto Usuyama. A scholar is included among the top collaborators of Naoto Usuyama 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 Naoto Usuyama. Naoto Usuyama is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

14 of 14 papers shown

1.

Usuyama, Naoto, et al.. (2025). Boltzmann Attention Sampling for Image Analysis with Small Objects. 25950–25959.

2.

Usuyama, Naoto, Cliff Wong, Sheng Zhang, Tristan Naumann, & Hoifung Poon. (2025). Biomedical Natural Language Processing in the Era of Large Language Models. PubMed. 8(1). 471–490. 3 indexed citations

3.

Ooka, Tadao, Naoto Usuyama, Michihito Kyo, et al.. (2024). Integrated-omics analysis with explainable deep networks on pathobiology of infant bronchiolitis. npj Systems Biology and Applications. 10(1). 93–93. 1 indexed citations

4.

Bifulco, Carlo, H. Poon, Naoto Usuyama, et al.. (2024). 1942O Application of GigaPath: An open-weight billion-parameter AI foundation model based on a novel vision transformer architecture for cancer mutation prediction and TME analysis. Annals of Oncology. 35. S1127–S1127.

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.

Liu, Fangyu, Qianchu Liu, Shruthi Bannur, et al.. (2023). Compositional Zero-Shot Domain Transfer with Text-to-Text Models. Transactions of the Association for Computational Linguistics. 11. 1097–1113. 2 indexed citations

8.

Liu, Qianchu, Stephanie L. Hyland, Shruthi Bannur, et al.. (2023). Exploring the Boundaries of GPT-4 in Radiology. 14414–14445. 14 indexed citations

9.

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

10.

Usuyama, Naoto, et al.. (2023). LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day. 28541–28564. 1 indexed citations

11.

Zhang, Sheng, Cliff Wong, Naoto Usuyama, et al.. (2021). Modular Self-Supervision for Document-Level Relation Extraction. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 5291–5302. 5 indexed citations

12.

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 →

13.

Usuyama, Naoto, et al.. (2019). Fast and accurate medication identification. npj Digital Medicine. 2(1). 10–10. 30 indexed citations

14.

Usuyama, Naoto, Yuichi Shiraishi, Yusuke Sato, et al.. (2014). HapMuC: somatic mutation calling using heterozygous germ line variants near candidate mutations. Bioinformatics. 30(23). 3302–3309. 15 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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