Nicolas Padoy

7.0k total citations · 1 hit paper
124 papers, 3.1k citations indexed

About

Nicolas Padoy is a scholar working on Surgery, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Nicolas Padoy has authored 124 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Surgery, 34 papers in Computer Vision and Pattern Recognition and 31 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Nicolas Padoy's work include Surgical Simulation and Training (59 papers), Anatomy and Medical Technology (16 papers) and Radiomics and Machine Learning in Medical Imaging (16 papers). Nicolas Padoy is often cited by papers focused on Surgical Simulation and Training (59 papers), Anatomy and Medical Technology (16 papers) and Radiomics and Machine Learning in Medical Imaging (16 papers). Nicolas Padoy collaborates with scholars based in France, Italy and Germany. Nicolas Padoy's co-authors include Didier Mutter, Jacques Marescaux, Andru Putra Twinanda, Michel de Mathelin, Pietro Mascagni, Nassir Navab, Hubertus Feußner, Tobias Blum, Gregory D. Hager and Chinedu Innocent Nwoye and has published in prestigious journals such as Annals of Surgery, Scientific Reports and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Nicolas Padoy

108 papers receiving 3.0k citations

Hit Papers

align trajectories sort by overperformance

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.

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

2016 579 citations Andru Putra Twinanda, Didier Mutter et al. IEEE Transactions on Medical Imaging profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Nicolas Padoy	1.8k	1.1k	790	554	444	124	3.1k
Guy Rosman	888 0.5×	456 0.4×	543 0.7×	238 0.4×	609 1.4×	85	2.5k
Stefanie Speidel	948 0.5×	783 0.7×	894 1.1×	298 0.5×	173 0.4×	126	2.4k
Andrew J. Hung	2.3k 1.2×	1.4k 1.3×	318 0.4×	273 0.5×	458 1.0×	124	3.8k
Didier Mutter	4.4k 2.4×	1.4k 1.3×	994 1.3×	1.9k 3.5×	217 0.5×	230	6.6k
Eli Konen	1.2k 0.7×	490 0.4×	338 0.4×	213 0.4×	469 1.1×	182	4.8k
Jan Egger	796 0.4×	951 0.9×	898 1.1×	82 0.1×	260 0.6×	164	2.9k
Thomas Neumuth	903 0.5×	446 0.4×	229 0.3×	218 0.4×	78 0.2×	188	1.9k
Hubertus Feußner	3.5k 1.9×	723 0.7×	611 0.8×	585 1.1×	49 0.1×	253	4.9k
Luc Soler	2.0k 1.1×	1.8k 1.6×	1.7k 2.2×	494 0.9×	41 0.1×	139	4.2k
Samuel Kadoury	879 0.5×	1.3k 1.2×	611 0.8×	166 0.3×	266 0.6×	163	4.4k

Countries citing papers authored by Nicolas Padoy

Since Specialization

Citations

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

Fields of papers citing papers by Nicolas Padoy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nicolas Padoy

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

All Works

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20 of 20 papers shown

Paisant, Anita, Agnès Rode, Riccardo Sartoris, et al.. (2025). Improving risk stratification and detection of early HCC using ultrasound-based deep learning models. JHEP Reports. 7(10). 101510–101510.

Nwoye, Chinedu Innocent, R. P. Jagadeesh Chandra Bose, Giorgio Carlino, et al.. (2025). Surgical text-to-image generation. Pattern Recognition Letters. 190. 73–80. 2 indexed citations

Bai, Long, Junyi Wang, Kun Yuan, et al.. (2025). EndoChat: Grounded multimodal large language model for endoscopic surgery. Medical Image Analysis. 107(Pt A). 103789–103789. 1 indexed citations

Chen, K. H., Joël L. Lavanchy, Didier Mutter, et al.. (2025). When do they StOP?: A first step toward automatically identifying team communication in the operating room. International Journal of Computer Assisted Radiology and Surgery. 20(7). 1371–1379. 1 indexed citations

Collins, Toby, Daniel A. Hashimoto, Silvana Perretta, et al.. (2025). Surgeons’ awareness, expectations, and involvement with artificial intelligence: a survey pre and post the GPT era. European Journal of Surgical Oncology. 51(12). 110525–110525.

Yuan, Kun, Tong Yu, Joël L. Lavanchy, et al.. (2025). Learning multi-modal representations by watching hundreds of surgical video lectures. Medical Image Analysis. 105. 103644–103644. 4 indexed citations

Padoy, Nicolas, et al.. (2025). Introducing surgical workflow recognition in orthopaedic surgery with timestamp supervision. Computers in Biology and Medicine. 197(Pt A). 110995–110995. 1 indexed citations

Alapatt, Deepak, Tong Yu, Sergio Alfieri, et al.. (2024). Applications of artificial intelligence in surgery: clinical, technical, and governance considerations. Cirugía Española (English Edition). 102. S66–S71.

Mutter, Didier, et al.. (2024). Overcoming Dimensional Collapse in Self-Supervised Contrastive Learning for Medical Image Segmentation. 1–5.

10.

Seeliger, Barbara, Pier Francesco Alesina, Laurent Brunaud, et al.. (2024). Development of an ontology for laparoscopic transabdominal adrenalectomy via a comprehensive modified Delphi survey and its validation on a multicentric pilot data set for surgical training and future video analysis with machine learning algorithms. British journal of surgery. 111(6).

11.

Nwoye, Chinedu Innocent, et al.. (2023). Rendezvous in time: an attention-based temporal fusion approach for surgical triplet recognition. International Journal of Computer Assisted Radiology and Surgery. 18(6). 1053–1059. 16 indexed citations

12.

Mascagni, Pietro, Deepak Alapatt, Alfonso Lapergola, et al.. (2023). Early-stage clinical evaluation of real-time artificial intelligence assistance for laparoscopic cholecystectomy. British journal of surgery. 111(1). 19 indexed citations

13.

Yu, Tong, Pietro Mascagni, Juan M. Verde, et al.. (2023). Live laparoscopic video retrieval with compressed uncertainty. Medical Image Analysis. 88. 102866–102866. 4 indexed citations

14.

Alapatt, Deepak, et al.. (2023). Dissecting self-supervised learning methods for surgical computer vision. Medical Image Analysis. 88. 102844–102844. 28 indexed citations

15.

Lampert, Thomas, et al.. (2023). Preliminary stage in the development of an artificial intelligence algorithm: Variations between 100 surgeons in phase annotation in a video of internal fixation of distal radius fracture. Orthopaedics & Traumatology Surgery & Research. 109(6). 103564–103564. 9 indexed citations

16.

Mascagni, Pietro, Deepak Alapatt, Maria S. Altieri, et al.. (2022). Computer vision in surgery: from potential to clinical value. npj Digital Medicine. 5(1). 163–163. 102 indexed citations

17.

Gangi, Afshin, et al.. (2022). Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room. Medical Image Analysis. 80. 102525–102525. 8 indexed citations

18.

Rosa, Benoît, et al.. (2021). A Kinematic Bottleneck Approach for Pose Regression of Flexible Surgical Instruments Directly From Images. IEEE Robotics and Automation Letters. 6(2). 2938–2945. 21 indexed citations

19.

Ward, Thomas M., Pietro Mascagni, Amin Madani, et al.. (2021). Surgical data science and artificial intelligence for surgical education. Journal of Surgical Oncology. 124(2). 221–230. 54 indexed citations

20.

Twinanda, Andru Putra, et al.. (2018). RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations. IEEE Transactions on Medical Imaging. 38(4). 1069–1078. 72 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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