Suguru Yasutomi

519 total citations
18 papers, 347 citations indexed

About

Suguru Yasutomi is a scholar working on Pediatrics, Perinatology and Child Health, Computer Vision and Pattern Recognition and Epidemiology. According to data from OpenAlex, Suguru Yasutomi has authored 18 papers receiving a total of 347 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Pediatrics, Perinatology and Child Health, 5 papers in Computer Vision and Pattern Recognition and 4 papers in Epidemiology. Recurrent topics in Suguru Yasutomi's work include Fetal and Pediatric Neurological Disorders (5 papers), Congenital Heart Disease Studies (4 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Suguru Yasutomi is often cited by papers focused on Fetal and Pediatric Neurological Disorders (5 papers), Congenital Heart Disease Studies (4 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Suguru Yasutomi collaborates with scholars based in Japan and United Kingdom. Suguru Yasutomi's co-authors include Akira Sakai, Ryuji Hamamoto, Masaaki Komatsu, Ken Asada, Syuzo Kaneko, Kanto Shozu, Ai Dozen, Hidenori Machino, Akihiko Sekizawa and Ryu Matsuoka and has published in prestigious journals such as IEEE Access, IEEE Transactions on Knowledge and Data Engineering and Applied Sciences.

In The Last Decade

Suguru Yasutomi

15 papers receiving 339 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Suguru Yasutomi Japan 8 147 85 83 82 70 18 347
Huaxuan Wen China 12 142 1.0× 227 2.7× 126 1.5× 47 0.6× 106 1.5× 33 540
Yimei Liao China 6 68 0.5× 138 1.6× 60 0.7× 27 0.3× 48 0.7× 19 271
Lisa M. Koch United Kingdom 6 105 0.7× 166 2.0× 106 1.3× 39 0.5× 38 0.5× 13 320
Sandra Smith United Kingdom 4 77 0.5× 143 1.7× 107 1.3× 39 0.5× 38 0.5× 5 271
Yihua He China 13 77 0.5× 120 1.4× 44 0.5× 33 0.4× 180 2.6× 38 424
Elisenda Bonet-Carné Spain 12 148 1.0× 277 3.3× 99 1.2× 51 0.6× 32 0.5× 28 511
David Coronado-Gutiérrez Spain 6 71 0.5× 133 1.6× 82 1.0× 41 0.5× 16 0.2× 11 251
Amir H. Abdi Canada 10 167 1.1× 17 0.2× 67 0.8× 14 0.2× 25 0.4× 23 392
Benjamin Hou United Kingdom 8 131 0.9× 48 0.6× 111 1.3× 58 0.7× 16 0.2× 16 310
Maria Chiara Fiorentino Italy 10 88 0.6× 112 1.3× 77 0.9× 30 0.4× 11 0.2× 23 288

Countries citing papers authored by Suguru Yasutomi

Since Specialization
Citations

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

Fields of papers citing papers by Suguru Yasutomi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Suguru Yasutomi

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

All Works

18 of 18 papers shown
1.
Yasutomi, Suguru & Toshihisa Tanaka. (2025). Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders With Mutual Information Constraints. IEEE Transactions on Knowledge and Data Engineering. 37(5). 3001–3014.
2.
Sakai, Akira, et al.. (2025). Establishment of High-Precision Ultrasound Diagnosis Methods Based on the Introduction of Deep Learning. IEEE Reviews in Biomedical Engineering. PP. 1–15.
3.
Sakai, Akira, et al.. (2024). Assessment of Tail-Cutting in Frozen Albacore (Thunnus alalunga) Through Ultrasound Inspection and Chemical Analysis. Foods. 13(23). 3860–3860. 1 indexed citations
4.
Sakai, Akira, et al.. (2023). Machine Learning Approach for Frozen Tuna Freshness Inspection Using Low-Frequency A-Mode Ultrasound. IEEE Access. 11. 107379–107393. 6 indexed citations
5.
Sakai, Akira, Tatsuya Arakaki, Mayumi Tokunaka, et al.. (2022). OP05.08: Automated evaluation of pulmonary venous drainage into the left atrium in the second trimester ultrasound screening of the fetal heart. Ultrasound in Obstetrics and Gynecology. 60(S1). 63–63. 1 indexed citations
6.
Sakai, Akira, Masaaki Komatsu, Ryu Matsuoka, et al.. (2022). Medical Professional Enhancement Using Explainable Artificial Intelligence in Fetal Cardiac Ultrasound Screening. Biomedicines. 10(3). 551–551. 32 indexed citations
7.
Komatsu, Masaaki, Akira Sakai, Hideki Arima, et al.. (2022). Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning. Biomedicines. 10(5). 1082–1082. 11 indexed citations
8.
Komatsu, Masaaki, Akira Sakai, Ai Dozen, et al.. (2021). Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging. Biomedicines. 9(7). 720–720. 75 indexed citations
9.
Yasutomi, Suguru, Tatsuya Arakaki, Ryu Matsuoka, et al.. (2021). Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows. Applied Sciences. 11(3). 1127–1127. 24 indexed citations
10.
Komatsu, Masaaki, Akira Sakai, Ryu Matsuoka, et al.. (2021). Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning. Applied Sciences. 11(1). 371–371. 87 indexed citations
11.
Shozu, Kanto, Masaaki Komatsu, Akira Sakai, et al.. (2020). Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos. Biomolecules. 10(12). 1691–1691. 34 indexed citations
12.
Dozen, Ai, Masaaki Komatsu, Akira Sakai, et al.. (2020). Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information. Biomolecules. 10(11). 1526–1526. 60 indexed citations
13.
Yasutomi, Suguru, Akira Sakai, Masaaki Komatsu, et al.. (2019). Unsupervised Shadow Detection for Ultrasound Images by Deep Learning. IEICE Technical Report; IEICE Tech. Rep.. 118(412). 151–156. 1 indexed citations
14.
Matsuoka, Ryu, Masaaki Komatsu, Akira Sakai, et al.. (2019). P08.01: A novel deep learning based system for fetal cardiac screening. Ultrasound in Obstetrics and Gynecology. 54(S1). 177–178. 6 indexed citations
15.
Matsuoka, Ryu, Tatsuya Arakaki, Mayumi Tokunaka, et al.. (2019). OP15.04: Novel AI‐guided ultrasound screening system for fetal heart can demonstrate findings in timeline diagram. Ultrasound in Obstetrics and Gynecology. 54(S1). 134–134. 7 indexed citations
16.
Yasutomi, Suguru, Tatsuya Arakaki, & Ryuji Hamamoto. (2019). Shadow Detection for Ultrasound Images Using Unlabeled Data and Synthetic Shadows. 1 indexed citations
17.
Kojima, A., et al.. (2016). Sparse Kernel Regression Based on Nonparametric Bayesian Model. IEICE Technical Report; IEICE Tech. Rep.. 115(522). 335–340.
18.
Yasutomi, Suguru & Toshihisa Tanaka. (2014). Parameter estimation for von mises-fisher mixture model via Gaussian distribution. 104. 1–5. 1 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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