Ryutaro Tanno

2.5k total citations · 1 hit paper
15 papers, 550 citations indexed

About

Ryutaro Tanno is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ryutaro Tanno has authored 15 papers receiving a total of 550 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Artificial Intelligence and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ryutaro Tanno's work include Machine Learning and Data Classification (5 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Artificial Intelligence in Healthcare and Education (3 papers). Ryutaro Tanno is often cited by papers focused on Machine Learning and Data Classification (5 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Artificial Intelligence in Healthcare and Education (3 papers). Ryutaro Tanno collaborates with scholars based in United Kingdom, United States and Netherlands. Ryutaro Tanno's co-authors include Daniel C. Alexander, Ardavan Saeedi, Nathan Silberman, Swami Sankaranarayanan, Enrico Kaden, Aurobrata Ghosh, Antonio Criminisi, Stamatios N. Sotiropoulos, Francesco Grussu and Alberto Bizzi and has published in prestigious journals such as Nature Medicine, Nature Communications and PLoS ONE.

In The Last Decade

Ryutaro Tanno

15 papers receiving 542 citations

Hit 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.

Generative models improve fairness of medical classifiers under distribution shifts

2024 55 citations Sofia Ira Ktena, Olivia Wiles et al. Nature Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ryutaro Tanno United Kingdom	10	250	225	131	50	36	15	550
Fuhua Yan China	13	294 1.2×	148 0.7×	41 0.3×	31 0.6×	9 0.3×	43	556
Dzhoshkun I. Shakir United Kingdom	9	221 0.9×	131 0.6×	160 1.2×	27 0.5×	49 1.4×	22	532
Murat Seçkin Ayhan Germany	7	204 0.8×	272 1.2×	124 0.9×	50 1.0×	10 0.3×	12	534
Arnaldo Mayer Israel	13	222 0.9×	107 0.5×	256 2.0×	9 0.2×	67 1.9×	45	584
Iñaki Soto‐Rey Germany	7	219 0.9×	143 0.6×	107 0.8×	28 0.6×	5 0.1×	28	521
Shekoofeh Azizi United States	10	333 1.3×	350 1.6×	362 2.8×	64 1.3×	10 0.3×	25	876
Aaron Loh United States	3	189 0.8×	227 1.0×	86 0.7×	32 0.6×	6 0.2×	3	375
Maria Inês Meyer Belgium	5	220 0.9×	88 0.4×	177 1.4×	17 0.3×	11 0.3×	5	368
Clara Mosquera-Lopez United States	16	59 0.2×	181 0.8×	107 0.8×	33 0.7×	8 0.2×	33	634

Countries citing papers authored by Ryutaro Tanno

Since Specialization

Citations

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

Fields of papers citing papers by Ryutaro Tanno

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryutaro Tanno

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

All Works

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15 of 15 papers shown

Ktena, Sofia Ira, Olivia Wiles, Sylvestre-Alvise Rebuffi, et al.. (2024). Generative models improve fairness of medical classifiers under distribution shifts. Nature Medicine. 30(4). 1166–1173. 55 indexed citations breakdown →

Figini, Matteo, Felice D’Arco, Godwin Ogbole, et al.. (2023). Low-field magnetic resonance image enhancement via stochastic image quality transfer. Medical Image Analysis. 87. 102807–102807. 16 indexed citations

Chien, Isabel, Tim Regan, Ángel Enrique, et al.. (2023). Deep learning for the prediction of clinical outcomes in internet-delivered CBT for depression and anxiety. PLoS ONE. 18(11). e0272685–e0272685. 4 indexed citations

Zhang, Le, Ryutaro Tanno, Moucheng Xu, et al.. (2023). Learning from multiple annotators for medical image segmentation. Pattern Recognition. 138. 109400–109400. 21 indexed citations

Castro, Daniel C., Ryutaro Tanno, Anton Schwaighofer, et al.. (2022). Active label cleaning for improved dataset quality under resource constraints. Nature Communications. 13(1). 1161–1161. 55 indexed citations

Sudre, Carole H., Christian F. Baumgartner, Adrian V. Dalca, et al.. (2022). Uncertainty for Safe Utilization of Machine Learning in Medical Imaging. Lecture notes in computer science. 5 indexed citations

Sudre, Carole H., Roxane Licandro, Christian Baumgärtner, et al.. (2021). Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. Lecture notes in computer science. 20 indexed citations

Zhang, Le, Ryutaro Tanno, Moucheng Xu, et al.. (2020). Disentangling Human Error from Ground Truth in Segmentation of Medical Images. Pure Amsterdam UMC. 33. 15750–15762. 1 indexed citations

Tanno, Ryutaro, Daniel E. Worrall, Enrico Kaden, et al.. (2020). Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI. NeuroImage. 225. 117366–117366. 65 indexed citations

10.

Sudre, Carole H., Tal Arbel, Christian F. Baumgartner, et al.. (2020). Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis. Lecture notes in computer science. 36 indexed citations

11.

Tax, Chantal M. W., Francesco Grussu, Enrico Kaden, et al.. (2019). Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms. NeuroImage. 195. 285–299. 65 indexed citations

12.

Tanno, Ryutaro, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, & Nathan Silberman. (2019). Learning From Noisy Labels by Regularized Estimation of Annotator Confusion. 11236–11245. 129 indexed citations

13.

Tanno, Ryutaro, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, & Aditya V. Nori. (2018). Adaptive Neural Trees. arXiv (Cornell University). 6166–6175. 8 indexed citations

14.

Bragman, Felix, Ryutaro Tanno, Zach Eaton-Rosen, et al.. (2018). Quality control in radiotherapy-treatment planning using multi-task learning and uncertainty estimation. 4 indexed citations

15.

Alexander, Daniel C., Darko Zikic, Aurobrata Ghosh, et al.. (2017). Image quality transfer and applications in diffusion MRI. NeuroImage. 152. 283–298. 66 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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