2026 Samuel Kadoury: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	37	10567	10269	421	402	193	7823

Overview

Samuel Kadoury is affiliated with Polytechnique Montréal in Canada. Their research activities encompass multiple disciplines, primarily focusing on medicine and computer science. They have a significant publication record in specialized subfields including radiology, nuclear medicine and imaging, biomedical engineering, computer vision and pattern recognition, artificial intelligence, and pulmonary and respiratory medicine.

Their research work covers a variety of topics central to advancements in medical imaging and related technologies. These topics include:

Radiomics and Machine Learning in Medical Imaging
Advanced Radiotherapy Techniques
Medical Imaging and Analysis
Medical Imaging Techniques and Applications
Spectroscopy Techniques in Biomedical and Chemical Research
AI in cancer detection
Medical Image Segmentation Techniques

Kadoury has contributed to numerous recent papers. Notable publications include:

"Deep learning workflow in radiology: a primer", 2020, Insights into Imaging
"Deep Learning: An Update for Radiologists", 2021, Radiographics
"Overview of Machine Learning: Part 2", 2020, Neuroimaging Clinics of North America
"Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation", 2020, PLoS Medicine
"Prediction of in-plane organ deformation during free-breathing radiotherapy via discriminative spatial transformer networks", 2020, Medical Image Analysis

Their frequent collaborators include William Le, An Tang, Liset Vázquez Romaguera, Cynthia Ménard, and Emmanuel Montagnon. These collaborations reflect a consistent partnership with researchers actively engaged in overlapping research domains.

Kadoury's research is often published in several leading venues related to medical imaging and computational analysis. The most frequent publication venues include:

arXiv (Cornell University)
Medical Image Analysis
Physics in Medicine and Biology
International Journal of Computer Assisted Radiology and Surgery
International Journal of Radiation Oncology*Biology*Physics

Best Publications

The Liver Tumor Segmentation Benchmark (LiTS)

Patrick Bilic;Patrick Ferdinand Christ;Eugene Vorontsov;Grzegorz Chlebus

1146
Citations
The Importance of Skip Connections in Biomedical Image Segmentation

Michal Drozdzal;Eugene Vorontsov;Gabriel Chartrand;Samuel Kadoury

1125
Citations
Deep Learning: A Primer for Radiologists

Gabriel Chartrand;Phillip M Cheng;Eugene Vorontsov;Michal Drozdzal

1100
Citations
Magnetic Resonance Imaging/Ultrasound Fusion Guided Prostate Biopsy Improves Cancer Detection Following Transrectal Ultrasound Biopsy and Correlates With Multiparametric Magnetic Resonance Imaging

Peter A. Pinto;Paul H. Chung;Ardeshir R. Rastinehad;Angelo A. Baccala

612
Citations
Intravoxel incoherent motion MR imaging for prostate cancer: An evaluation of perfusion fraction and diffusion coefficient derived from different b-value combinations

Yuxi Pang;Baris Turkbey;Marcelino Bernardo;Jochen Kruecker

283
Citations
Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

Michal Drozdzal;Michal Drozdzal;Gabriel Chartrand;Eugene Vorontsov;Mahsa Shakeri

262
Citations
Liver segmentation: indications, techniques and future directions.

Akshat Gotra;Akshat Gotra;Lojan Sivakumaran;Gabriel Chartrand;Kim-Nhien Vu

213
Citations
Robust, accurate and fast automatic segmentation of the spinal cord.

Benjamin De Leener;Samuel Kadoury;Julien Cohen-Adad;Julien Cohen-Adad

194
Citations
On orthogonality and learning recurrent networks with long term dependencies

Eugene Vorontsov;Chiheb Trabelsi;Samuel Kadoury;Chris Pal

183
Citations
Multimodality image fusion-guided procedures: technique, accuracy, and applications.

Nadine Abi-Jaoudeh;Jochen Kruecker;Samuel Kadoury;Hicham Kobeiter

166
Citations
Deep learning workflow in radiology: a primer.

Emmanuel Montagnon;Milena Cerny;Alexandre Cadrin-Chênevert;Vincent Hamilton

164
Citations
The Importance of Skip Connections in Biomedical Image Segmentation

Michal Drozdzal;Eugene Vorontsov;Gabriel Chartrand;Samuel Kadoury

160
Citations
Deep Learning: An Update for Radiologists.

Phillip M Cheng;Emmanuel Montagnon;Rikiya Yamashita;Ian Pan

147
Citations
D'Amico risk stratification correlates with degree of suspicion of prostate cancer on multiparametric magnetic resonance imaging.

Ardeshir R Rastinehad;Angelo A Baccala;Paul H Chung;Juan M Proano

139
Citations
Liver lesion segmentation informed by joint liver segmentation

Eugene Vorontsov;An Tang;Chris Pal;Samuel Kadoury

139
Citations
Sub-cortical brain structure segmentation using F-CNN'S

Mahsa Shaken;Stavros Tsogkas;Enzo Ferrante;Sarah Lippe

137
Citations
Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases

Eugene Vorontsov;Milena Cerny;Philippe Régnier;Lisa Di Jorio

129
Citations
Real-time FDG PET Guidance during Biopsies and Radiofrequency Ablation Using Multimodality Fusion with Electromagnetic Navigation

Aradhana M. Venkatesan;Samuel Kadoury;Nadine Abi-Jaoudeh;Elliot B. Levy

127
Citations
A versatile 3D reconstruction system of the spine and pelvis for clinical assessment of spinal deformities

Samuel Kadoury;Farida Cheriet;Catherine Laporte;Hubert Labelle

124
Citations
Automatic Segmentation of the Spinal Cord and Spinal Canal Coupled With Vertebral Labeling

Benjamin De Leener;Julien Cohen-Adad;Samuel Kadoury

109
Citations
Convolutional networks for kidney segmentation in contrast-enhanced CT scans

William E. Thong;Samuel Kadoury;Nicolas Piché;Christopher J. Pal

103
Citations
A Novel System for the 3-D Reconstruction of the Human Spine and Rib Cage From Biplanar X-Ray Images

F. Cheriet;C. Laporte;S. Kadoury;H. Labelle

98
Citations
On orthogonality and learning recurrent networks with long term dependencies

Eugene Vorontsov;Chiheb Trabelsi;Samuel Kadoury;Chris Pal

97
Citations
Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

Michal Drozdzal;Gabriel Chartrand;Eugene Vorontsov;Lisa Di Jorio

5
Citations

Frequent Co-Authors

Raman Kashyap Polytechnique Montréal

Chris Pal Polytechnique Montréal

Nikos Paragios CentraleSupélec

Sylvain Martel Polytechnique Montréal

Iasonas Kokkinos University College London

Pingkun Yan Rensselaer Polytechnic Institute

Julien Cohen-Adad Polytechnique Montréal

Paul C. Boutros University of California, Los Angeles

Jan Kautz Nvidia (United States)

Marc Modat King's College London

External Links

Personal website of Samuel Kadoury

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

If you’re considering a career in computer science, there are many educational pathways available beyond a traditional bachelor’s degree. For those who want to enter the workforce quickly or boost specific skills, certificate programs that pay well offer targeted training in high-demand areas such as cybersecurity, coding, and information technology. These programs can help you gain in-demand skills and valuable credentials in a short time.

If you’re looking to earn an advanced degree efficiently, several universities offer some of the quickest masters degree online options. These programs are designed for busy professionals and can typically be completed in as little as one year.

Some degrees yield a greater return on investment than others. Exploring the most worthwhile masters degrees can help you choose specializations that are consistently in high demand, such as data science, software engineering, or artificial intelligence.

Finally, online associate degree programs provide a flexible and affordable way to jumpstart your tech career or prepare for further study. These programs can set you on the path to entry-level jobs or lay the groundwork for more advanced degrees in the future.