Kiyoshi Namba

1.1k total citations
25 papers, 831 citations indexed

About

Kiyoshi Namba is a scholar working on Pathology and Forensic Medicine, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Kiyoshi Namba has authored 25 papers receiving a total of 831 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Pathology and Forensic Medicine, 10 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Artificial Intelligence. Recurrent topics in Kiyoshi Namba's work include Breast Lesions and Carcinomas (10 papers), AI in cancer detection (9 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). Kiyoshi Namba is often cited by papers focused on Breast Lesions and Carcinomas (10 papers), AI in cancer detection (9 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). Kiyoshi Namba collaborates with scholars based in Japan, United States and United Kingdom. Kiyoshi Namba's co-authors include Hidemi Furusawa, Hiroshi Nakahara, Chiaki Tanaka, Yukiko Yasuda, Futoshi Akiyama, Ryohei Nakayama, Sharon Thomsen, Achiude Bendet, Yoshikazu Uchiyama and Koji Yamamoto and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cancer and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Kiyoshi Namba

21 papers receiving 792 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kiyoshi Namba Japan 14 440 352 209 147 137 25 831
Christiane Marx Germany 14 381 0.9× 166 0.5× 187 0.9× 146 1.0× 96 0.7× 18 656
Kyu Ran Cho South Korea 20 672 1.5× 175 0.5× 297 1.4× 231 1.6× 267 1.9× 75 1.1k
Hongna Tan China 14 432 1.0× 78 0.2× 124 0.6× 188 1.3× 146 1.1× 29 688
I. Schreer Germany 15 392 0.9× 123 0.3× 505 2.4× 243 1.7× 391 2.9× 51 923
John J. Gisvold United States 12 259 0.6× 97 0.3× 428 2.0× 154 1.0× 341 2.5× 21 841
Yu‐Mee Sohn South Korea 16 462 1.1× 213 0.6× 156 0.7× 190 1.3× 133 1.0× 49 879
Dianne Georgian-Smith United States 20 512 1.2× 91 0.3× 595 2.8× 202 1.4× 464 3.4× 31 1.1k
L.J. Yeoman United Kingdom 14 183 0.4× 58 0.2× 278 1.3× 181 1.2× 234 1.7× 24 646
Nicky H. G. M. Peters Netherlands 14 1.1k 2.5× 224 0.6× 350 1.7× 110 0.7× 257 1.9× 22 1.3k
H. Madjar Germany 21 534 1.2× 201 0.6× 595 2.8× 284 1.9× 541 3.9× 76 1.6k

Countries citing papers authored by Kiyoshi Namba

Since Specialization
Citations

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

Fields of papers citing papers by Kiyoshi Namba

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kiyoshi Namba

This figure shows the co-authorship network connecting the top 25 collaborators of Kiyoshi Namba. A scholar is included among the top collaborators of Kiyoshi Namba 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 Kiyoshi Namba. Kiyoshi Namba 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.
Nakayama, Ryohei, et al.. (2023). Potential Usefulness a Coronal View using an Automated Breast Ultrasound System in Detecting Breast Lesions. SHILAP Revista de lepidopterología. 20(1). 57–63.
2.
Nakayama, Ryohei, et al.. (2020). Diagnostic performance of coronal view in comparison with transverse view of three-dimensional automated breast ultrasound. Acta Radiologica. 62(1). 27–33. 3 indexed citations
3.
4.
Yamaguchi, Rin, Hidemi Furusawa, Hiroshi Nakahara, et al.. (2007). Clinicopathological study of invasive ductal carcinoma with large central acellular zone: Special reference to magnetic resonance imaging findings. Pathology International. 58(1). 26–30. 16 indexed citations
5.
Furusawa, Hidemi, Kiyoshi Namba, Hiroshi Nakahara, et al.. (2007). The evolving non-surgical ablation of breast cancer: Mr Guided focused ultrasound (MRgFUS). Breast Cancer. 14(1). 55–58. 137 indexed citations
6.
Gombos, Eva C., Daniel F. Kacher, Hidemi Furusawa, & Kiyoshi Namba. (2006). Breast Focused Ultrasound Surgery With Magnetic Resonance Guidance. Topics in Magnetic Resonance Imaging. 17(3). 181–188. 32 indexed citations
7.
Furusawa, Hidemi, Kiyoshi Namba, Sharon Thomsen, et al.. (2006). Magnetic Resonance–Guided Focused Ultrasound Surgery of Breast Cancer: Reliability and Effectiveness. Journal of the American College of Surgeons. 203(1). 54–63. 216 indexed citations
8.
Nakayama, Ryohei, Yoshikazu Uchiyama, Koji Yamamoto, Ryo Watanabe, & Kiyoshi Namba. (2006). Computer-Aided Diagnosis Scheme Using a Filter Bank for Detection of Microcalcification Clusters in Mammograms. IEEE Transactions on Biomedical Engineering. 53(2). 273–283. 82 indexed citations
9.
Nakayama, Ryohei, Ryoji Watanabe, Kiyoshi Namba, et al.. (2006). Computer-Aided Diagnosis Scheme for Identifying Histological Classification of Clustered Microcalcifications by Use of Follow-up Magnification Mammograms. Academic Radiology. 13(10). 1219–1228. 9 indexed citations
10.
Namba, Kiyoshi, et al.. (2004). Intraductal biopsy for diagnosis and treatment of intraductal lesions of the breast. Cancer. 101(10). 2164–2169. 32 indexed citations
11.
Nakayama, Ryohei, Yoshikazu Uchiyama, Ryoji Watanabe, et al.. (2004). Computer‐aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms. Medical Physics. 31(4). 789–799. 32 indexed citations
12.
Watanabe, Ryoji, et al.. (2003). . Nihon Nyugan Kenshin Gakkaishi (Journal of Japan Association of Breast Cancer Screening). 12(2). 147–151.
13.
Nakahara, Hiroshi, Kiyoshi Namba, Ryoji Watanabe, et al.. (2003). A comparison of mr imaging, galactography and ultrasonography in patients with nipple discharge. Breast Cancer. 10(4). 320–329. 60 indexed citations
14.
Namba, Kiyoshi, et al.. (2002). . Nihon Nyugan Kenshin Gakkaishi (Journal of Japan Association of Breast Cancer Screening). 11(2). 172–178. 2 indexed citations
15.
16.
Nakahara, Hiroshi, Kiyoshi Namba, Ryoji Watanabe, et al.. (2001). Three-dimensional mr imaging of mammographically detected suspicious microcalcifications. Breast Cancer. 8(2). 116–124. 22 indexed citations
17.
Nakahara, Hiroshi, Kiyoshi Namba, Ryoji Watanabe, et al.. (1998). Computer-Aided Diagnosis (CAD) for Mammography: Preliminary Results. Breast Cancer. 5(4). 401–405. 7 indexed citations
18.
Scott, Colin, G. J. den Ottolander, David Swirsky, et al.. (1995). Recommended Procedures for the Classification of Acute Leukaemias. Leukemia & lymphoma. 18(sup1). 1–12. 37 indexed citations
19.
Scott, Colin, G. J. den Ottolander, David Swirsky, et al.. (1993). Recommended Procedures for the Classification of Acute Leukaemias. Leukemia & lymphoma. 11(1-2). 37–50. 22 indexed citations
20.
Makita, Masujiro, Kiyoshi Namba, Haruo Sugano, et al.. (1991). Duct endoscopy and endoscopic biopsy in the evaluation of nipple discharge. Breast Cancer Research and Treatment. 18(3). 179–187. 49 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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