Amal Farag

2.3k citations
34 papers · 885 · h-index 11

Impact in

Papers in

Amal Farag

31 papers receiving 846 citations

Peers

Amal Farag
Comparison fields: 5 of 91
  • Radiology, Nuclear Medicine and Imaging 587
  • Computer Vision and Pattern Recognition 426
  • Artificial Intelligence 263
  • Neurology 67
  • Pulmonary and Respiratory Medicine 235
Replace Jyh-Shyan Lin with:
Jyh-Shyan Lin United States
Fangfang Han China
Renchao Jin China
Ricardo J. Ferrari Brazil
Ester Bonmati United Kingdom
Kunio Doi United States
Shanhui Sun United States
Ali Gooya United Kingdom
Sharib Ali United Kingdom
Jixiang Guo China
Amal Farag relative to Jyh-Shyan Lin United States Jyh-Shyan Lin's profile →
Citations per field
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Jyh-Shyan Lin · 1×
Citations per year

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-authors

The 25 scholars most cited alongside Amal Farag, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Amal Farag Line = papers co-authored together Amal Farag links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018229
2 2016133
3 2015128
4 2013115
5 200346
6 201138
7 201032
8 201723
9 200616
10 201915
11 201310
12 201410
13 20109
14 20068
15 20128
16 20107
17 20137
18 20127
19 20176
20 20116

About Amal Farag

Amal Farag is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence and Oncology, having authored 34 papers that have together received 885 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (15 papers), Lung Cancer Diagnosis and Treatment (13 papers), Radiomics and Machine Learning in Medical Imaging (10 papers), Advanced Neural Network Applications (5 papers), Medical Imaging Techniques and Applications (4 papers), Image Retrieval and Classification Techniques (4 papers), AI in cancer detection (4 papers) and Colorectal Cancer Screening and Detection (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (587 citations), Computer Vision and Pattern Recognition (426 citations), Artificial Intelligence (263 citations), Neurology (67 citations) and Pulmonary and Respiratory Medicine (235 citations). Amal Farag has collaborated with scholars based in United States, Egypt and Japan. Frequent 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. Their work appears in journals such as IEEE Transactions on Image Processing, International Journal of Computer Assisted Radiology and Surgery, Medical Image Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence and Lecture notes in computer science.

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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