Christian S. Perone

765 total citations
9 papers, 361 citations indexed

About

Christian S. Perone is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, Christian S. Perone has authored 9 papers receiving a total of 361 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 3 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Biomedical Engineering. Recurrent topics in Christian S. Perone's work include Medical Imaging and Analysis (3 papers), Brain Tumor Detection and Classification (2 papers) and AI in cancer detection (2 papers). Christian S. Perone is often cited by papers focused on Medical Imaging and Analysis (3 papers), Brain Tumor Detection and Classification (2 papers) and AI in cancer detection (2 papers). Christian S. Perone collaborates with scholars based in Canada, United States and France. Christian S. Perone's co-authors include Julien Cohen‐Adad, Evan Calabrese, Syed M. Adil, G. Allan Johnson, Gary P. Cofer, Shivanand P. Lad, Sergey Zagoruyko, Stefano Pini, Pascal Sati and Daniel S. Reich and has published in prestigious journals such as The Astrophysical Journal, Scientific Reports and Magnetic Resonance Imaging.

In The Last Decade

Christian S. Perone

9 papers receiving 353 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Christian S. Perone Canada	7	112	64	60	51	35	9	361
Carole Frindel France	14	200 1.8×	95 1.5×	30 0.5×	37 0.7×	23 0.7×	49	472
Delia P. McGarry United States	5	211 1.9×	66 1.0×	23 0.4×	108 2.1×	27 0.8×	8	432
Aichi Chien United States	17	84 0.8×	50 0.8×	50 0.8×	138 2.7×	54 1.5×	61	971
Sharif Amit Kamran United States	16	282 2.5×	52 0.8×	53 0.9×	65 1.3×	13 0.4×	47	636
Nasif Zaman United States	16	261 2.3×	45 0.7×	60 1.0×	56 1.1×	20 0.6×	78	707
Frédéric Cervenansky France	8	267 2.4×	168 2.6×	33 0.6×	64 1.3×	18 0.5×	16	428
Aleksandar Peulić Serbia	9	121 1.1×	53 0.8×	64 1.1×	34 0.7×	10 0.3×	40	348
Karl Sjöstrand Denmark	11	153 1.4×	56 0.9×	68 1.1×	76 1.5×	8 0.2×	28	451
Fernando Pérez‐García United Kingdom	9	226 2.0×	63 1.0×	141 2.4×	107 2.1×	11 0.3×	17	503
Nandhini Santhanam Germany	3	205 1.8×	94 1.5×	175 2.9×	79 1.5×	9 0.3×	4	519

Countries citing papers authored by Christian S. Perone

Since Specialization

Citations

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

Fields of papers citing papers by Christian S. Perone

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christian S. Perone. 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 Christian S. Perone. The network helps show where Christian S. Perone may publish in the future.

Co-authorship network of co-authors of Christian S. Perone

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

All Works

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9 of 9 papers shown

1.

Pini, Stefano, et al.. (2023). Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts. 10069–10075. 16 indexed citations

2.

Perone, Christian S., et al.. (2019). Open-source pipeline for multi-class segmentation of the spinal cord with deep learning. Magnetic Resonance Imaging. 64. 21–27. 13 indexed citations

3.

Perone, Christian S. & Julien Cohen‐Adad. (2019). Promises and limitations of deep learning for medical image segmentation. 2. 1–1. 36 indexed citations

4.

Perone, Christian S., Evan Calabrese, & Julien Cohen‐Adad. (2018). Spinal cord gray matter segmentation using deep dilated convolutions. Scientific Reports. 8(1). 5966–5966. 86 indexed citations

5.

Calabrese, Evan, Syed M. Adil, Gary P. Cofer, et al.. (2018). Postmortem diffusion MRI of the entire human spinal cord at microscopic resolution. NeuroImage Clinical. 18. 963–971. 26 indexed citations

6.

Perone, Christian S., et al.. (2018). AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks. Scientific Reports. 8(1). 3816–3816. 90 indexed citations

7.

Perone, Christian S., et al.. (2018). perone/medicaltorch: Release v0.2. Zenodo (CERN European Organization for Nuclear Research). 6 indexed citations

8.

Szkody, Paula, Anjum S. Mukadam, B. T. Gänsicke, et al.. (2010). ANALYZING THE LOW STATE OF EF ERIDANI WITHHUBBLE SPACE TELESCOPEULTRAVIOLET SPECTRA. The Astrophysical Journal. 716(2). 1531–1540. 5 indexed citations

9.

Perone, Christian S.. (2009). Pyevolve. 4(1). 12–20. 83 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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