Simon Graham

3.2k total citations
28 papers, 1.0k citations indexed

About

Simon Graham is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Simon Graham has authored 28 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 16 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Oncology. Recurrent topics in Simon Graham's work include AI in cancer detection (20 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Digital Imaging for Blood Diseases (5 papers). Simon Graham is often cited by papers focused on AI in cancer detection (20 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Digital Imaging for Blood Diseases (5 papers). Simon Graham collaborates with scholars based in United Kingdom, Egypt and United States. Simon Graham's co-authors include Nasir Rajpoot, David Snead, Shan E Ahmed Raza, Fayyaz Minhas, Qi Dou, Hao Chen, Pheng‐Ann Heng, Mohsin Bilal, Jevgenij Gamper and Muhammad Shaban and has published in prestigious journals such as SHILAP Revista de lepidopterología, British Journal of Cancer and IEEE Transactions on Medical Imaging.

In The Last Decade

Simon Graham

27 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Simon Graham United Kingdom 14 770 590 389 183 154 28 1.0k
Yee‐Wah Tsang United Kingdom 5 857 1.1× 551 0.9× 429 1.1× 189 1.0× 224 1.5× 6 1.2k
Maschenka Balkenhol Netherlands 13 685 0.9× 530 0.9× 298 0.8× 151 0.8× 139 0.9× 21 906
Ruchika Verma United States 10 583 0.8× 499 0.8× 463 1.2× 79 0.4× 161 1.0× 30 1.0k
Péter Bándi Netherlands 8 617 0.8× 421 0.7× 292 0.8× 116 0.6× 125 0.8× 15 779
Abhishek Vahadane India 8 1.0k 1.3× 590 1.0× 708 1.8× 115 0.6× 276 1.8× 10 1.3k
Guillaume Jaume United States 11 577 0.7× 401 0.7× 210 0.5× 114 0.6× 107 0.7× 14 885
N. K. Timofeeva Netherlands 4 717 0.9× 471 0.8× 292 0.8× 137 0.7× 143 0.9× 10 963
Luke Geneslaw United States 4 1.1k 1.4× 773 1.3× 401 1.0× 340 1.9× 182 1.2× 4 1.5k
Bassem Ben Cheikh France 6 443 0.6× 299 0.5× 344 0.9× 157 0.9× 80 0.5× 18 778
Wouter Bulten Netherlands 8 752 1.0× 530 0.9× 275 0.7× 157 0.9× 103 0.7× 9 998

Countries citing papers authored by Simon Graham

Since Specialization
Citations

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

Fields of papers citing papers by Simon Graham

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simon Graham

This figure shows the co-authorship network connecting the top 25 collaborators of Simon Graham. A scholar is included among the top collaborators of Simon Graham 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 Simon Graham. Simon Graham 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.
Evans, Harriet, et al.. (2025). Evaluating the pathological and clinical implications of errors made by an artificial intelligence colon biopsy screening tool. BMJ Open Gastroenterology. 12(1). e001649–e001649. 1 indexed citations
2.
Graham, Simon, et al.. (2024). HoverFast: an accurate, high-throughput, clinicallydeployable nuclear segmentation tool for brightfield digital pathologyimages. The Journal of Open Source Software. 9(101). 7022–7022. 1 indexed citations
3.
Shephard, Adam, Mostafa Jahanifar, Ruoyu Wang, et al.. (2024). An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer. Warwick Research Archive Portal (University of Warwick). 1–5. 3 indexed citations
4.
Lashen, Ayat, Noorul Wahab, Michael S. Toss, et al.. (2024). Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence. Cancers. 16(22). 3849–3849. 1 indexed citations
5.
6.
Jahanifar, Mostafa, Adam Shephard, Simon Graham, et al.. (2024). Mitosis detection, fast and slow: Robust and efficient detection of mitotic figures. Medical Image Analysis. 94. 103132–103132. 20 indexed citations
7.
Wahab, Noorul, Michael S. Toss, Asmaa Ibrahim, et al.. (2023). Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence. British Journal of Cancer. 129(11). 1747–1758. 22 indexed citations
8.
Ibrahim, Asmaa, Mostafa Jahanifar, Noorul Wahab, et al.. (2023). Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application. Modern Pathology. 37(3). 100416–100416. 11 indexed citations
9.
Wahab, Noorul, Michael S. Toss, Asmaa Ibrahim, et al.. (2023). Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence. Modern Pathology. 36(10). 100254–100254. 5 indexed citations
10.
Wahab, Noorul, Michael S. Toss, Islam M. Miligy, et al.. (2023). AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer. npj Precision Oncology. 7(1). 122–122. 8 indexed citations
11.
Asif, Amina, Kashif Rajpoot, Simon Graham, et al.. (2023). Unleashing the potential of AI for pathology: challenges and recommendations. The Journal of Pathology. 260(5). 564–577. 18 indexed citations
12.
Lu, Wenqi, Ayat Lashen, Noorul Wahab, et al.. (2023). AI‐based intra‐tumor heterogeneity score of Ki67 expression as a prognostic marker for early‐stage ER+/HER2− breast cancer. The Journal of Pathology Clinical Research. 10(1). e346–e346. 4 indexed citations
13.
Jahanifar, Mostafa, et al.. (2023). Accurate segmentation of nuclear instances using a double-stage neural network. Warwick Research Archive Portal (University of Warwick). 27–27. 1 indexed citations
14.
Graham, Simon, Quoc Dang Vu, Mostafa Jahanifar, et al.. (2022). TIAToolbox as an end-to-end library for advanced tissue image analytics. SHILAP Revista de lepidopterología. 2(1). 120–120. 52 indexed citations
15.
16.
Minhas, Fayyaz, et al.. (2021). SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images. Medical Image Analysis. 77. 102337–102337. 21 indexed citations
17.
Fraz, Muhammad Moazam, et al.. (2019). FABnet: feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer. Neural Computing and Applications. 32(14). 9915–9928. 46 indexed citations
18.
Graham, Simon, Hao Chen, Jevgenij Gamper, et al.. (2018). MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images. Medical Image Analysis. 52. 199–211. 217 indexed citations
19.
Raza, Shan E Ahmed, Linda Cheung, Muhammad Shaban, et al.. (2018). Micro-Net: A unified model for segmentation of various objects in microscopy images. Medical Image Analysis. 52. 160–173. 174 indexed citations
20.
Graham, Simon. (2000). Rapid prototyping: a key to fast tracking design to manufacture. Assembly Automation. 20(4). 291–294. 5 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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