David Farnell

965 total citations
22 papers, 383 citations indexed

About

David Farnell is a scholar working on Oncology, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, David Farnell has authored 22 papers receiving a total of 383 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Oncology, 7 papers in Molecular Biology and 7 papers in Artificial Intelligence. Recurrent topics in David Farnell's work include AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Colorectal Cancer Screening and Detection (3 papers). David Farnell is often cited by papers focused on AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Colorectal Cancer Screening and Detection (3 papers). David Farnell collaborates with scholars based in Canada, United States and Germany. David Farnell's co-authors include C. Blake Gilks, Ali Bashashati, David G. Huntsman, G. Graff, Joan M. Spellman, John M. Yanni, Lori K. Weimer, Milton T. Brady, Steven T. Miller and Daniel A. Gamache and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

David Farnell

21 papers receiving 378 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Farnell Canada 10 97 95 90 69 69 22 383
E.C.M. Wisse-Brekelmans Netherlands 8 126 1.3× 130 1.4× 50 0.6× 25 0.4× 48 0.7× 8 588
Mariël Brinkhuis Netherlands 12 44 0.5× 87 0.9× 16 0.2× 29 0.4× 104 1.5× 33 455
Gareth Bryson United Kingdom 9 22 0.2× 32 0.3× 57 0.6× 16 0.2× 45 0.7× 20 274
M. M. M. Pahlplatz Netherlands 12 20 0.2× 28 0.3× 95 1.1× 10 0.1× 45 0.7× 28 447
H.M. Ruitenberg Netherlands 8 13 0.1× 20 0.2× 38 0.4× 17 0.2× 47 0.7× 12 509
Mijke Bol Netherlands 7 41 0.4× 75 0.8× 32 0.4× 15 0.2× 38 0.6× 10 381
Sonya M. Diakiw Australia 12 10 0.1× 109 1.1× 61 0.7× 212 3.1× 29 0.4× 18 546
Thierry Pécot United States 13 26 0.3× 9 0.1× 67 0.7× 19 0.3× 63 0.9× 34 502
Darius Dasevičius Lithuania 15 4 0.0× 92 1.0× 132 1.5× 39 0.6× 105 1.5× 28 547

Countries citing papers authored by David Farnell

Since Specialization
Citations

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

Fields of papers citing papers by David Farnell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Farnell

This figure shows the co-authorship network connecting the top 25 collaborators of David Farnell. A scholar is included among the top collaborators of David Farnell 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 David Farnell. David Farnell 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.
Farnell, David, et al.. (2025). Benchmarking histopathology foundation models for ovarian cancer bevacizumab treatment response prediction from whole slide images. Discover Oncology. 16(1). 196–196. 1 indexed citations
2.
Chu, Jenny E., David Farnell, David J. Schaeffer, et al.. (2025). A Deep Learning Framework for Classification of Neuroendocrine Neoplasm Whole Slide Images. Cancers. 17(18). 2991–2991.
3.
Zhang, Allen, Alberto Contreras‐Sanz, Martin Köbel, et al.. (2024). Learning generalizable AI models for multi-center histopathology image classification. npj Precision Oncology. 8(1). 151–151. 12 indexed citations
4.
Farahani, Hossein, Maryam Asadi, Matthew O. Wiens, et al.. (2024). AI-based histopathology image analysis reveals a distinct subset of endometrial cancers. Nature Communications. 15(1). 4973–4973. 13 indexed citations
5.
Ji, Jennifer X., Lien Hoang, Dawn R. Cochrane, et al.. (2024). The unique metabolome of clear cell ovarian carcinoma. The Journal of Pathology. 264(2). 160–173. 3 indexed citations
6.
Farahani, Hossein, David Farnell, James T. Topham, et al.. (2024). A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images. American Journal Of Pathology. 194(12). 2302–2312. 3 indexed citations
7.
Farahani, Hossein, David Farnell, Allen Zhang, et al.. (2022). Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images. Modern Pathology. 35(12). 1983–1990. 42 indexed citations
8.
Gill, Sharlene, et al.. (2022). Well-Differentiated Grade 3 Neuroendocrine Tumors. Pancreas. 51(7). 756–762. 5 indexed citations
9.
Teng, Katie, Matthew J. Ford, Yuqi Li, et al.. (2021). Modeling High-Grade Serous Ovarian Carcinoma Using a Combination of In Vivo Fallopian Tube Electroporation and CRISPR-Cas9–Mediated Genome Editing. Cancer Research. 81(20). 5147–5160. 13 indexed citations
10.
Chahal, Daljeet, Trana Hussaini, David Farnell, Roland G. Nádor, & Eric M. Yoshida. (2021). Isolated Liver Rejection After Lung and Combined Kidney-Liver Transplantation: A Case Report. Transplantation Proceedings. 53(4). 1333–1336. 2 indexed citations
11.
Farnell, David, et al.. (2021). Spray coagulation with snare-tip versus argon plasma coagulation: An ex vivo study evaluating tissue effects. SHILAP Revista de lepidopterología. 9(6). E790–E795. 3 indexed citations
12.
Cochrane, Dawn R., Jennifer Pors, Gian Luca Negri, et al.. (2020). Whole-proteome analysis of mesonephric-derived cancers describes new potential biomarkers. Human Pathology. 108. 1–11. 7 indexed citations
13.
Pai, Rish K., Rish K. Pai, Ian Brown, et al.. (2020). The significance of histological activity measurements in immune checkpoint inhibitor colitis. Alimentary Pharmacology & Therapeutics. 53(1). 150–159. 14 indexed citations
14.
Wang, Yemin, Clara Salamanca, Christine Chow, et al.. (2020). Establishment and characterization of VOA1066 cells: An undifferentiated endometrial carcinoma cell line. PLoS ONE. 15(10). e0240412–e0240412. 3 indexed citations
15.
Kim, Soyoun Rachel, Samuel Leung, Dawn R. Cochrane, et al.. (2020). Molecular subtypes of clear cell carcinoma of the endometrium: Opportunities for prognostic and predictive stratification. Gynecologic Oncology. 158(1). 3–11. 89 indexed citations
16.
Cochrane, Dawn R., Basile Tessier‐Cloutier, Samuel Leung, et al.. (2019). Expression of L1 retrotransposon open reading frame protein 1 in gynecologic cancers. Human Pathology. 92. 39–47. 7 indexed citations
17.
Farnell, David, David Huntsman, & Ali Bashashati. (2019). The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies. Histopathology. 76(1). 171–177. 9 indexed citations
18.
Farnell, David, et al.. (2015). A Retrospective Case Study of Two Consecutive Liver Biopsies in a Patient With Obliterative Portal Venopathy. American Journal of Clinical Pathology. 144(suppl 2). A352–A352. 1 indexed citations
19.
Farnell, David. (2011). Nucleotide Excision Repair in the Three Domains of Life. 2(1). 3 indexed citations
20.
Yanni, John M., Steven T. Miller, Lori K. Weimer, et al.. (1996). The In Vitro and In Vivo Ocular Pharmacology of Olopatadine (AL-4943A), an Effective Anti-Allergic/Antihistaminic Agent. Journal of Ocular Pharmacology and Therapeutics. 12(4). 389–400. 96 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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