Maryam Sadeghi

566 total citations
23 papers, 354 citations indexed

About

Maryam Sadeghi is a scholar working on Oncology, Computer Vision and Pattern Recognition and Cell Biology. According to data from OpenAlex, Maryam Sadeghi has authored 23 papers receiving a total of 354 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Oncology, 7 papers in Computer Vision and Pattern Recognition and 7 papers in Cell Biology. Recurrent topics in Maryam Sadeghi's work include Cutaneous Melanoma Detection and Management (10 papers), melanin and skin pigmentation (7 papers) and Optical Coherence Tomography Applications (5 papers). Maryam Sadeghi is often cited by papers focused on Cutaneous Melanoma Detection and Management (10 papers), melanin and skin pigmentation (7 papers) and Optical Coherence Tomography Applications (5 papers). Maryam Sadeghi collaborates with scholars based in Canada, Germany and Austria. Maryam Sadeghi's co-authors include M. Stella Atkins, Tim K. Lee, Harvey Lui, David I. McLean, Mark S. Drew, Paul Wighton, Michael Friebe, David B. Clarke, Murray Hong and Nelofar Kureshi and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Nephrology Dialysis Transplantation and BMC Medical Education.

In The Last Decade

Maryam Sadeghi

23 papers receiving 341 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maryam Sadeghi Canada 11 222 160 79 77 55 23 354
Mariana Francisco Portugal 3 274 1.2× 218 1.4× 58 0.7× 40 0.5× 57 1.0× 5 337
Jason Hagerty United States 11 273 1.2× 220 1.4× 76 1.0× 52 0.7× 21 0.4× 26 402
Maher I. Rajab Saudi Arabia 7 132 0.6× 105 0.7× 81 1.0× 54 0.7× 25 0.5× 17 300
Roberta B. Oliveira Brazil 6 388 1.7× 292 1.8× 125 1.6× 73 0.9× 43 0.8× 22 540
Reda Kasmi Algeria 9 429 1.9× 333 2.1× 77 1.0× 70 0.9× 46 0.8× 11 514
Nabin K. Mishra United States 6 305 1.4× 250 1.6× 58 0.7× 50 0.6× 14 0.3× 8 403
Jinman Kim Australia 7 312 1.4× 270 1.7× 124 1.6× 70 0.9× 22 0.4× 12 483
Joseph M. Malters United States 10 447 2.0× 260 1.6× 82 1.0× 139 1.8× 64 1.2× 13 546
Nudrat Nida Pakistan 10 194 0.9× 188 1.2× 162 2.1× 45 0.6× 16 0.3× 23 436
P. C. Siddalingaswamy India 12 244 1.1× 223 1.4× 165 2.1× 47 0.6× 22 0.4× 34 538

Countries citing papers authored by Maryam Sadeghi

Since Specialization
Citations

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

Fields of papers citing papers by Maryam Sadeghi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maryam Sadeghi

This figure shows the co-authorship network connecting the top 25 collaborators of Maryam Sadeghi. A scholar is included among the top collaborators of Maryam Sadeghi 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 Maryam Sadeghi. Maryam Sadeghi 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.
Sadeghi, Maryam, Naghmeh Mahmoodian, Zdenka Hrušková, et al.. (2024). Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis. Kidney International Reports. 10(2). 457–465. 2 indexed citations
2.
Sadeghi, Maryam, Andreas Kronbichler, Zdenka Hrušková, et al.. (2023). #4466 EXPLAINABILITY OF A DEEP LEARNING BASED CLASSIFICATION MODEL FOR ANCA-ASSOCIATED GLOMERULONEPHRITIS. Nephrology Dialysis Transplantation. 38(Supplement_1). 1 indexed citations
3.
Sadeghi, Maryam, Pedro C. Neto, Enrica Paradiso, et al.. (2023). Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space. Neuroinformatics. 21(3). 615–630. 3 indexed citations
4.
Sadeghi, Maryam, Pedro C. Neto, Enrica Paradiso, et al.. (2022). Automatic 2D to 3D localization of histological mouse brain sections in the reference atlas using deep learning. Data Archiving and Networked Services (DANS). 94–94. 1 indexed citations
5.
Sadeghi, Maryam, et al.. (2019). Feasibility test of Dynamic Cooling for detection of small tumors in IR thermographic breast imaging. Current Directions in Biomedical Engineering. 5(1). 397–399. 3 indexed citations
6.
Illanes, Alfredo, et al.. (2019). Patch Based Texture Classification of Thyroid Ultrasound Images using Convolutional Neural Network. PubMed. 2019. 5828–5831. 12 indexed citations
7.
Sadeghi, Maryam, et al.. (2019). Feedback-based Self-improving CNN Algorithm for Breast Cancer Lymph Node Metastasis Detection in Real Clinical Environment. PubMed. 2019. 7212–7215. 2 indexed citations
8.
Nili, Mahmoud, et al.. (2018). 自己充填コンクリートの分離抵抗を調べるための自動画像解析プロセス【JST・京大機械翻訳】. Magazine of Concrete Research. 70(8). 390–399. 3 indexed citations
9.
Nili, Mahmoud, et al.. (2017). Automatic image analysis process to appraise segregation resistance of self-consolidating concrete. Magazine of Concrete Research. 70(8). 390–399. 10 indexed citations
10.
Clarke, David B., Nelofar Kureshi, Murray Hong, Maryam Sadeghi, & Ryan C.N. D’Arcy. (2016). Simulation-based training for burr hole surgery instrument recognition. BMC Medical Education. 16(1). 153–153. 18 indexed citations
11.
Sadeghi, Maryam, et al.. (2013). Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions. IEEE Transactions on Medical Imaging. 32(5). 849–861. 81 indexed citations
12.
Drew, Mark S., et al.. (2013). Automatic Detection of Blue-White Veil by Discrete Colour Matching in Dermoscopy Images. Lecture notes in computer science. 16(Pt 3). 453–460. 17 indexed citations
13.
Drew, Mark S., et al.. (2012). Automated Pre-processing Method for Dermoscopic Images and its Application to Pigmented Skin Lesion Segmentation.. 158–163. 9 indexed citations
14.
Drew, Mark S., et al.. (2012). Intrinsic Melanin and Hemoglobin Colour Components for Skin Lesion Malignancy Detection. Lecture notes in computer science. 15(Pt 1). 315–322. 17 indexed citations
15.
Sadeghi, Maryam, Tim K. Lee, David I. McLean, Harvey Lui, & M. Stella Atkins. (2012). Oriented Pattern Analysis for Streak Detection in Dermoscopy Images. Lecture notes in computer science. 15(Pt 1). 298–306. 6 indexed citations
16.
Drew, Mark S., et al.. (2012). Automated Pre–processing Method for Dermoscopic Images and its Application to Pigmented Skin Lesion Segmentation. Color and Imaging Conference. 20(1). 158–163. 14 indexed citations
17.
Sadeghi, Maryam, et al.. (2010). A novel method for detection of pigment network in dermoscopic images using graphs. Computerized Medical Imaging and Graphics. 35(2). 137–143. 73 indexed citations
18.
Sadeghi, Maryam, et al.. (2009). Hands-free interactive image segmentation using eyegaze. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7260. 72601H–72601H. 17 indexed citations
19.
Wighton, Paul, Maryam Sadeghi, Tim K. Lee, & M. Stella Atkins. (2009). A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting. Lecture notes in computer science. 12(Pt 2). 1108–1115. 36 indexed citations
20.
Sadeghi, Maryam & Mahmood Fathy. (2006). A Low-Cost Occlusion Handling Using a Novel Feature in Congested Traffic Images. 522–527. 3 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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