Ruqayya Awan

3.5k total citations
8 papers, 163 citations indexed

About

Ruqayya Awan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ruqayya Awan has authored 8 papers receiving a total of 163 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ruqayya Awan's work include AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Colorectal Cancer Screening and Detection (3 papers). Ruqayya Awan is often cited by papers focused on AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Colorectal Cancer Screening and Detection (3 papers). Ruqayya Awan collaborates with scholars based in United Kingdom, Qatar and Germany. Ruqayya Awan's co-authors include Nasir Rajpoot, David Snead, Korsuk Sirinukunwattana, D. B. A. Epstein, Uvais Qidwai, Somaya Al-Máadeed, Muhammad Shaban, Ayesha Azam, Fayyaz Minhas and Clare Verrill and has published in prestigious journals such as PLoS ONE, Scientific Reports and Cytometry Part A.

In The Last Decade

Ruqayya Awan

8 papers receiving 163 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ruqayya Awan United Kingdom	5	122	105	60	48	23	8	163
Dig Vijay Kumar Yarlagadda United States	7	123 1.0×	79 0.8×	46 0.8×	54 1.1×	16 0.7×	9	181
Brandon Ginley United States	8	195 1.6×	86 0.8×	52 0.9×	102 2.1×	31 1.3×	27	354
Wei-Hsiang Yu Taiwan	6	166 1.4×	179 1.7×	50 0.8×	45 0.9×	19 0.8×	10	268
Brendon Lutnick United States	7	159 1.3×	78 0.7×	43 0.7×	85 1.8×	30 1.3×	18	306
Deepak Anand India	7	111 0.9×	108 1.0×	40 0.7×	36 0.8×	24 1.0×	15	199
Ronnachai Jaroensri United States	5	200 1.6×	151 1.4×	57 0.9×	61 1.3×	24 1.0×	7	328
Zhaoyang Xu China	6	164 1.3×	133 1.3×	30 0.5×	66 1.4×	27 1.2×	16	221
Niccolò Marini Switzerland	9	157 1.3×	102 1.0×	27 0.5×	74 1.5×	27 1.2×	20	213
Matthew A. Suriawinata United States	4	226 1.9×	223 2.1×	131 2.2×	52 1.1×	22 1.0×	4	320
Mason McGough United States	3	151 1.2×	84 0.8×	38 0.6×	55 1.1×	28 1.2×	4	196

Countries citing papers authored by Ruqayya Awan

Since Specialization

Citations

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

Fields of papers citing papers by Ruqayya Awan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruqayya Awan

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

All Works

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8 of 8 papers shown

1.

Alsalemi, Abdullah, Mostafa Jahanifar, Ruqayya Awan, et al.. (2025). From traditional to deep learning approaches in whole slide image registration: A methodological review. Journal of Pathology Informatics. 19. 100512–100512. 1 indexed citations

2.

Awan, Ruqayya, et al.. (2024). Dual attention model with reinforcement learning for classification of histology whole-slide images. Computerized Medical Imaging and Graphics. 118. 102466–102466. 1 indexed citations

3.

Awan, Ruqayya, Shan E Ahmed Raza, Johannes Lotz, Nick Weiss, & Nasir Rajpoot. (2022). Deep feature based cross-slide registration. Computerized Medical Imaging and Graphics. 104. 102162–102162. 5 indexed citations

4.

Awan, Ruqayya, Ayesha Azam, Muhammad Shaban, et al.. (2021). Deep learning based digital cell profiles for risk stratification of urine cytology images. Cytometry Part A. 99(7). 732–742. 31 indexed citations

5.

Awan, Ruqayya, et al.. (2019). Glandular structure-guided classification of microscopic colorectal images using deep learning. Computers & Electrical Engineering. 85. 106450–106450. 7 indexed citations

6.

Awan, Ruqayya, et al.. (2018). Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?. PLoS ONE. 13(6). e0197431–e0197431. 10 indexed citations

7.

Awan, Ruqayya, Korsuk Sirinukunwattana, D. B. A. Epstein, et al.. (2017). Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology Images. Scientific Reports. 7(1). 16852–16852. 105 indexed citations

8.

Awan, Ruqayya, et al.. (2016). How divided is a cell? Eigenphase nuclei for classification of mitotic phase in cancer histology images. Qatar University QSpace (Qatar University). 6. 70–73. 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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