Amal Farag

2.3k total citations
34 papers, 885 citations indexed

About

Amal Farag is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Amal Farag has authored 34 papers receiving a total of 885 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 17 papers in Radiology, Nuclear Medicine and Imaging and 13 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Amal Farag's work include Medical Image Segmentation Techniques (15 papers), Lung Cancer Diagnosis and Treatment (13 papers) and Radiomics and Machine Learning in Medical Imaging (10 papers). Amal Farag is often cited by papers focused on Medical Image Segmentation Techniques (15 papers), Lung Cancer Diagnosis and Treatment (13 papers) and Radiomics and Machine Learning in Medical Imaging (10 papers). Amal Farag collaborates with scholars based in United States, Egypt and Japan. Amal Farag's co-authors include Ronald M. Summers, Le Lü, Holger R. Roth, Aly A. Farag, Evrim Türkbey, Hossam E. Abd El Munim, Nathan Lay, Andrew Sohn, Adam P. Harrison and James H. Graham and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Medical Image Analysis.

In The Last Decade

Amal Farag

31 papers receiving 846 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Amal Farag United States 11 587 426 263 235 111 34 885
Shih‐Chung B. Lo United States 14 497 0.8× 309 0.7× 511 1.9× 244 1.0× 135 1.2× 73 961
Shekoofeh Azizi United States 10 333 0.6× 362 0.8× 350 1.3× 103 0.4× 97 0.9× 25 876
Jyh-Shyan Lin United States 9 380 0.6× 297 0.7× 374 1.4× 193 0.8× 76 0.7× 25 823
Kyu-Hwan Jung South Korea 18 679 1.2× 226 0.5× 264 1.0× 215 0.9× 141 1.3× 38 1.1k
Fangfang Han China 14 599 1.0× 159 0.4× 242 0.9× 479 2.0× 117 1.1× 44 890
Sertan Serte Cyprus 15 733 1.2× 263 0.6× 447 1.7× 114 0.5× 53 0.5× 36 1.0k
Kelei He China 16 580 1.0× 441 1.0× 369 1.4× 135 0.6× 210 1.9× 25 1.1k
Miguel Souto Spain 17 455 0.8× 430 1.0× 435 1.7× 328 1.4× 109 1.0× 58 1.0k
Jinzheng Cai United States 11 408 0.7× 357 0.8× 383 1.5× 73 0.3× 101 0.9× 17 773
Hidefumi Kobatake Japan 19 481 0.8× 705 1.7× 454 1.7× 131 0.6× 164 1.5× 100 1.1k

Countries citing papers authored by Amal Farag

Since Specialization
Citations

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

Fields of papers citing papers by Amal Farag

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amal Farag

This figure shows the co-authorship network connecting the top 25 collaborators of Amal Farag. A scholar is included among the top collaborators of Amal Farag 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 Amal Farag. Amal Farag 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.
Roth, Holger R., Le Lü, Nathan Lay, et al.. (2018). Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Medical Image Analysis. 45. 94–107. 229 indexed citations
2.
Farag, Amal, et al.. (2018). Enhancing virtual colonoscopy with a new visualization measure. 294–297. 3 indexed citations
3.
Farag, Amal, et al.. (2017). Feature fusion for lung nodule classification. International Journal of Computer Assisted Radiology and Surgery. 12(10). 1809–1818. 23 indexed citations
4.
Farag, Amal, Le Lü, Holger R. Roth, et al.. (2016). A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling. IEEE Transactions on Image Processing. 26(1). 386–399. 133 indexed citations
5.
Roth, Holger R., Amal Farag, Le Lü, Evrim Türkbey, & Ronald M. Summers. (2015). Deep convolutional networks for pancreas segmentation in CT imaging. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9413. 94131G–94131G. 128 indexed citations
6.
Biasotti, Silvia, Andrea Cerri, Mostafa Abdelrahman, et al.. (2014). Retrieval and Classification on Textured 3D Models. ISTI Open Portal. 10 indexed citations
7.
Farag, Amal, Jiamin Liu, & Ronald M. Summers. (2014). Automatic segmentation of abdominal vessels for improved pancreas localization. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9037. 90371M–90371M. 3 indexed citations
8.
Farag, Amal. (2013). Variational approach for small-size lung nodule segmentation. 231. 81–84. 10 indexed citations
9.
Machado, José J. M., Alfredo Ferreira, Mostafa Abdelrahman, et al.. (2013). SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 7 indexed citations
10.
Farag, Amal, James H. Graham, & Aly A. Farag. (2013). Deformable models for random small-size objects: Case of lung nodules in CT tomography. 231. 1090–1093. 2 indexed citations
11.
Farag, Amal, Hossam E. Abd El Munim, James H. Graham, & Aly A. Farag. (2013). A Novel Approach for Lung Nodules Segmentation in Chest CT Using Level Sets. IEEE Transactions on Image Processing. 22(12). 5202–5213. 115 indexed citations
12.
13.
Farag, Amal, Asem Ali, Shireen Elhabian, & Aly A. Farag. (2011). Probability density estimation by linear combinations of Gaussian kernels- generalizations and algorithmic evaluation. 6491–6494. 1 indexed citations
14.
Farag, Amal, Shireen Elhabian, James Graham, Aly A. Farag, & Robert Falk. (2010). Toward Precise Pulmonary Nodule Descriptors for Nodule Type Classification. Lecture notes in computer science. 13(Pt 3). 626–633. 32 indexed citations
15.
Farag, Amal, et al.. (2010). Data-Driven Lung Nodule Models for Robust Nodule Detection in Chest CT. 19. 2588–2591. 7 indexed citations
16.
Farag, Amal, Ayman El‐Baz, Seniha Esen Yüksel, Mohamed Abou El‐Ghar, & Tarek El‐Diasty. (2006). A Framework for the Detection of Acute Renal Rejection with Dynamic Contrast Enhanced Magnetic Resonance Imaging. 418–421. 8 indexed citations
17.
El‐Baz, Ayman, Amal Farag, Georgy Gimel’farb, et al.. (2006). A Framework for Automatic Segmentation of Lung Nodules from Low Dose Chest CT Scans. 611–614. 16 indexed citations
18.
El‐Baz, Ayman, Amal Farag, & Georgy Gimel’farb. (2005). Stochastic Deformable Model. 76.1–76.10. 1 indexed citations
19.
El‐Baz, Ayman & Amal Farag. (2003). Pararmeter estimation in gibbs-markov image models. 934–942. 2 indexed citations
20.
Farag, Amal, et al.. (2002). Active contours: an overview with applications to motion artifact cancellation in MRI. 2. 644–647. 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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