Patrice Monkam

568 total citations
26 papers, 394 citations indexed

About

Patrice Monkam is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Patrice Monkam has authored 26 papers receiving a total of 394 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Computer Vision and Pattern Recognition and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Patrice Monkam's work include Radiomics and Machine Learning in Medical Imaging (9 papers), Medical Image Segmentation Techniques (9 papers) and COVID-19 diagnosis using AI (7 papers). Patrice Monkam is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), Medical Image Segmentation Techniques (9 papers) and COVID-19 diagnosis using AI (7 papers). Patrice Monkam collaborates with scholars based in China, United States and Macao. Patrice Monkam's co-authors include Shouliang Qi, Wei Qian, Wenkai Lu, Yudong Yao, Yile Ao, He Ma, Weiming Gao, Mingjie Xu, Fangfang Han and Chen Li and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Access and Measurement Science and Technology.

In The Last Decade

Patrice Monkam

24 papers receiving 380 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Patrice Monkam China 10 218 154 99 84 64 26 394
Haoran Dou China 14 242 1.1× 25 0.2× 256 2.6× 6 0.1× 42 0.7× 31 513
Máx Chacón Chile 12 78 0.4× 24 0.2× 130 1.3× 104 1.2× 11 0.2× 36 415
Kashif Javed Lone Pakistan 8 109 0.5× 17 0.1× 102 1.0× 20 0.2× 6 0.1× 15 273
Luana Batista da Cruz Brazil 7 172 0.8× 56 0.4× 97 1.0× 5 0.1× 8 0.1× 20 319
Natsuki Shimizu Japan 3 141 0.6× 25 0.2× 101 1.0× 101 1.2× 3 0.0× 10 347
Olsi Rama United States 7 389 1.8× 73 0.5× 99 1.0× 79 0.9× 58 0.9× 18 565
Mark Sak United States 9 176 0.8× 123 0.8× 133 1.3× 18 0.2× 5 0.1× 27 318
R. Dapp Germany 9 272 1.2× 19 0.1× 35 0.4× 51 0.6× 34 0.5× 22 378
Kristin McLeod Norway 9 106 0.5× 59 0.4× 64 0.6× 4 0.0× 5 0.1× 17 331
C Glide United States 2 257 1.2× 65 0.4× 71 0.7× 50 0.6× 23 0.4× 3 355

Countries citing papers authored by Patrice Monkam

Since Specialization
Citations

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

Fields of papers citing papers by Patrice Monkam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Patrice Monkam

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

All Works

20 of 20 papers shown
1.
Monkam, Patrice, et al.. (2025). MOM-BUS: a multi-output framework for precise breast lesion segmentation in ultrasound images. Measurement Science and Technology. 36(5). 55702–55702.
2.
Sun, Yu, et al.. (2025). MHASegNet: A multi-scale hybrid aggregation network of segmenting coronary artery from CCTA images. Journal of X-Ray Science and Technology. 33(5). 916–934.
3.
He, Dianning, et al.. (2024). Neuroimage analysis using artificial intelligence approaches: a systematic review. Medical & Biological Engineering & Computing. 62(9). 2599–2627. 7 indexed citations
4.
Monkam, Patrice, et al.. (2024). An Attention-Based Deep Learning Network for Predicting Platinum Resistance in Ovarian Cancer. IEEE Access. 12. 41000–41008. 6 indexed citations
5.
Li, Yuxuan, et al.. (2023). LVSnake: Accurate and robust left ventricle contour localization for myocardial infarction detection. Biomedical Signal Processing and Control. 85. 105076–105076. 4 indexed citations
6.
Monkam, Patrice, et al.. (2023). Annotation Cost Minimization for Ultrasound Image Segmentation using Cross-domain Transfer Learning. IEEE Journal of Biomedical and Health Informatics. 27(4). 1–11. 2 indexed citations
7.
Li, Yuxuan, et al.. (2023). IA-Noise2Noise: An Image Alignment Strategy for Echocardiography Despeckling. 1–3. 1 indexed citations
8.
Monkam, Patrice, et al.. (2022). An efficient annotated data generation method for echocardiographic image segmentation. Computers in Biology and Medicine. 149. 106090–106090. 2 indexed citations
9.
Jia, Zhuo, et al.. (2022). EMRNet: End-to-End Electrical Model Restoration Network. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–12. 10 indexed citations
10.
Li, Yuxuan, Wenkai Lu, & Patrice Monkam. (2022). Zero-Shot Learning for Real-Time Ultrasound Image Enhancement. 2022 IEEE International Ultrasonics Symposium (IUS). 1–4. 2 indexed citations
11.
Monkam, Patrice, et al.. (2022). A Disentanglement and Fusion Data Augmentation Approach for Echocardiography Segmentation. 2022 IEEE International Ultrasonics Symposium (IUS). 1–4. 1 indexed citations
12.
Monkam, Patrice, Wenkai Lu, Jing Wu, et al.. (2022). US-Net: A lightweight network for simultaneous speckle suppression and texture enhancement in ultrasound images. Computers in Biology and Medicine. 152. 106385–106385. 8 indexed citations
13.
Monkam, Patrice, et al.. (2022). Echocardiography Segmentation Based on Cross-modal Data Augmentation Method. 2022 IEEE International Ultrasonics Symposium (IUS). 1–3. 2 indexed citations
14.
Lu, Wenkai, et al.. (2021). Super-Resolution of Seismic Velocity Model Guided by Seismic Data. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–12. 18 indexed citations
15.
Monkam, Patrice, et al.. (2021). EasySpec: Automatic Specular Reflection Detection and Suppression From Endoscopic Images. IEEE Transactions on Computational Imaging. 7. 1031–1043. 12 indexed citations
16.
Cao, Yuzhu, Benedictor Alexander Nguchu, Patrice Monkam, et al.. (2021). Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis. Academic Radiology. 28(11). 1507–1523. 12 indexed citations
17.
Zhang, Baihua, Shouliang Qi, Patrice Monkam, et al.. (2019). Ensemble Learners of Multiple Deep CNNs for Pulmonary Nodules Classification Using CT Images. IEEE Access. 7. 110358–110371. 71 indexed citations
18.
Monkam, Patrice, Shouliang Qi, Mingjie Xu, et al.. (2018). Ensemble Learning of Multiple-View 3D-CNNs Model for Micro-Nodules Identification in CT Images. IEEE Access. 7. 5564–5576. 35 indexed citations
19.
Monkam, Patrice, et al.. (2018). CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images. BioMedical Engineering OnLine. 17(1). 96–96. 44 indexed citations
20.
Yuan, Shuai, et al.. (2017). Active contour model via local and global intensity information for image segmentation. 41. 5618–5623. 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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