Eri Matsuyama

657 total citations
25 papers, 471 citations indexed

About

Eri Matsuyama is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Eri Matsuyama has authored 25 papers receiving a total of 471 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 13 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Eri Matsuyama's work include AI in cancer detection (12 papers), Digital Radiography and Breast Imaging (9 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). Eri Matsuyama is often cited by papers focused on AI in cancer detection (12 papers), Digital Radiography and Breast Imaging (9 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). Eri Matsuyama collaborates with scholars based in Japan, Taiwan and Australia. Eri Matsuyama's co-authors include Du‐Yih Tsai, Yongbum Lee, Noriyuki Takahashi, Toshibumi Kinoshita, Kiyoshi Ishii, Hsian‐Min Chen, Megumi Takehara and Hideto Toyoshima and has published in prestigious journals such as Journal of Digital Imaging, Journal of Electronic Imaging and International Journal of Biomedical Imaging.

In The Last Decade

Eri Matsuyama

23 papers receiving 454 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eri Matsuyama Japan 8 208 112 110 100 76 25 471
Zhengang Jiang China 11 173 0.8× 33 0.3× 182 1.7× 134 1.3× 43 0.6× 99 486
Jianan Chen China 10 254 1.2× 66 0.6× 138 1.3× 224 2.2× 42 0.6× 32 580
Enqing Dong China 14 177 0.9× 47 0.4× 154 1.4× 193 1.9× 64 0.8× 72 586
Pei Dong China 11 263 1.3× 61 0.5× 103 0.9× 115 1.1× 17 0.2× 36 512
Stephen Chang Singapore 9 318 1.5× 64 0.6× 117 1.1× 129 1.3× 40 0.5× 20 577
Klaus D. Toennies Germany 11 378 1.8× 54 0.5× 124 1.1× 145 1.4× 38 0.5× 40 630
Chung‐Ming Wu Taiwan 5 242 1.2× 70 0.6× 194 1.8× 163 1.6× 38 0.5× 7 544
Yixiong Liang China 15 491 2.4× 123 1.1× 247 2.2× 278 2.8× 17 0.2× 57 774
Franck Marzani France 14 155 0.7× 61 0.5× 127 1.2× 74 0.7× 14 0.2× 57 569
Dongsheng Jiang China 9 233 1.1× 44 0.4× 65 0.6× 139 1.4× 13 0.2× 22 391

Countries citing papers authored by Eri Matsuyama

Since Specialization
Citations

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

Fields of papers citing papers by Eri Matsuyama

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eri Matsuyama

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

All Works

20 of 20 papers shown
1.
2.
Matsuyama, Eri, et al.. (2024). Performance Comparison of Vision Transformer- and CNN-Based Image Classification Using Cross Entropy: A Preliminary Application to Lung Cancer Discrimination from CT Images. Journal of Biomedical Science and Engineering. 17(9). 157–170. 1 indexed citations
3.
Matsuyama, Eri, et al.. (2024). Using Cross Entropy as a Performance Metric for Quantifying Uncertainty in DNN Image Classifiers: An Application to Classification of Lung Cancer on CT Images. Journal of Biomedical Science and Engineering. 17(1). 1–12. 7 indexed citations
4.
Matsuyama, Eri, et al.. (2023). A Breast Density Classification System for Mammography Considering Reliability Issues in Deep Learning. 13(3). 63–83. 1 indexed citations
5.
Matsuyama, Eri. (2021). A Novel Method for Automated Lung Region Segmentation in Chest X-Ray Images. Journal of Biomedical Science and Engineering. 14(6). 288–299. 4 indexed citations
6.
Matsuyama, Eri, Megumi Takehara, & Du‐Yih Tsai. (2020). Using a Wavelet-Based and Fine-Tuned Convolutional Neural Network for Classification of Breast Density in Mammographic Images. 10(1). 17–29. 18 indexed citations
7.
Matsuyama, Eri & Du‐Yih Tsai. (2018). Automated Classification of Lung Diseases in Computed Tomography Images Using a Wavelet Based Convolutional Neural Network. Journal of Biomedical Science and Engineering. 11(10). 263–274. 17 indexed citations
8.
Takahashi, Noriyuki, Toshibumi Kinoshita, Yongbum Lee, et al.. (2015). Z-score-based semi-quantitative analysis of the volume of the temporal horn of the lateral ventricle on brain CT images. Radiological Physics and Technology. 9(1). 69–76. 2 indexed citations
9.
Takahashi, Noriyuki, Yongbum Lee, Du‐Yih Tsai, et al.. (2013). An automated detection method for the MCA dot sign of acute stroke in unenhanced CT. Radiological Physics and Technology. 7(1). 79–88. 45 indexed citations
10.
Tsai, Du‐Yih, Eri Matsuyama, & Hsian‐Min Chen. (2013). Improving Image Quality in Medical Images Using a Combined Method of Undecimated Wavelet Transform and Wavelet Coefficient Mapping. International Journal of Biomedical Imaging. 2013. 1–11. 12 indexed citations
11.
12.
Matsuyama, Eri, et al.. (2012). A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising. Journal of Digital Imaging. 26(4). 748–758. 27 indexed citations
13.
Tsai, Du‐Yih, Eri Matsuyama, & Yongbum Lee. (2011). Quantitative images quality evaluation of digital medical imaging systems using mutual information. 28. 1515–1519. 2 indexed citations
14.
Matsuyama, Eri, et al.. (2010). Investigation of Noise-Resolution Tradeoff for Digital Radiographic Imaging: A Simulation Study. Journal of Software Engineering and Applications. 3(10). 926–932. 1 indexed citations
15.
Matsuyama, Eri, et al.. (2010). Using mutual information to evaluate performance of medical imaging systems. Health. 2(4). 279–285. 3 indexed citations
16.
Matsuyama, Eri. (2009). Mutual information-based evaluation of image quality with its preliminary application to assessment of medical imaging systems. Journal of Electronic Imaging. 18(3). 33011–33011. 5 indexed citations
17.
Matsuyama, Eri, Yongbum Lee, & Du‐Yih Tsai. (2008). Image Quality Evaluation of Digital Radiographs by Use of Transmitted Information Metric. IEICE technical report. Speech. 108(131). 9–14. 1 indexed citations
18.
Matsuyama, Eri, et al.. (2008). Physical characterization of digital radiological images by use of transmitted information metric. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6913. 69130V–69130V. 2 indexed citations
19.
Tsai, Du‐Yih, Yongbum Lee, & Eri Matsuyama. (2007). Information Entropy Measure for Evaluation of Image Quality. Journal of Digital Imaging. 21(3). 338–347. 280 indexed citations
20.
Lee, Yongbum, Du‐Yih Tsai, & Eri Matsuyama. (2007). A Simulation Study of Radiographic Image Quality Measurement Based on Transmitted Information. Japanese Journal of Radiological Technology. 63(3). 341–344. 3 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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