Jack Zeineh

1.1k total citations
14 papers, 103 citations indexed

About

Jack Zeineh is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Cancer Research. According to data from OpenAlex, Jack Zeineh has authored 14 papers receiving a total of 103 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 9 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Cancer Research. Recurrent topics in Jack Zeineh's work include AI in cancer detection (11 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Breast Cancer Treatment Studies (4 papers). Jack Zeineh is often cited by papers focused on AI in cancer detection (11 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Breast Cancer Treatment Studies (4 papers). Jack Zeineh collaborates with scholars based in United States. Jack Zeineh's co-authors include Gerardo Fernández, Michael Donovan, Anwar Raza, Sue Martin, Alberto M. Marchevsky, Viera Nelson, Timothy S. Greaves, Camilla J. Cobb, Faisal M. Khan and Carlos Cordon‐Cardo and has published in prestigious journals such as Journal of Clinical Oncology, Cancer Research and European Journal of Cancer.

In The Last Decade

Jack Zeineh

11 papers receiving 102 citations

Peers

Jack Zeineh

RNMI

PRM

ONCOL

Yibo Zhang China

Jizhen Li China

Xiaojun Yang China

Piotr Kędzierawski Poland

Mathilde Bateson Canada

Ningshan Li China

Zhou Dong China

Leonor Cerdá-Alberich Spain

Riham El Khouli United States

Yibo Zhang China

Jack Zeineh

47 ×0.9

83 ×2.5

RNMI

45 ×1.5

PRM

24 ×1.2

55 ×2.8

ONCOL

Citations per year, relative to Jack Zeineh Jack Zeineh (= 1×) peers Yibo Zhang

Countries citing papers authored by Jack Zeineh

Since Specialization

Citations

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

Fields of papers citing papers by Jack Zeineh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jack Zeineh

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

All Works

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14 of 14 papers shown

Donovan, Michael, et al.. (2024). Clinical validation of an Artificial-intelligent (AI) enabled digital test using the patients diagnostic breast biopsy to predict invasive breast cancer recurrence within 6-years. European Journal of Cancer. 200. 113832–113832.

Donovan, Michael, et al.. (2024). AI-enabled digital test to predict disease recurrence for patients with early-stage invasive breast cancer and performance in a MammaPrint low-risk cohort from the Netherlands with a median 6-year follow-up.. Journal of Clinical Oncology. 42(16_suppl). 1569–1569.

Fernández, Gerardo, Jack Zeineh, Marcel Prastawa, et al.. (2023). Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer. Clinical Breast Cancer. 24(2). 93–102.e6. 2 indexed citations

Fernández, Gerardo, Marcel Prastawa, Bahram Marami, et al.. (2022). Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years. Breast Cancer Research. 24(1). 93–93. 14 indexed citations

McKenzie, Andrew, Russell W. Hanson, Kristen Whitney, et al.. (2022). Artificial intelligence-derived neurofibrillary tangle burden is associated with antemortem cognitive impairment. Acta Neuropathologica Communications. 10(1). 157–157. 24 indexed citations

Donovan, Michael, et al.. (2021). Abstract PS6-09: An AI-digital breast cancer risk discrimination platform (PreciseDx) using a representative H&E image and selected clinical variables accurately categorized patients with oncotype Dx low risk recurrence scores (RS). Cancer Research. 81(4_Supplement). PS6–9. 1 indexed citations

Prastawa, Marcel, Brandon Veremis, Alexander Shtabsky, et al.. (2020). Abstract P3-08-11: The application of machine learning techniques to standardize breast cancer grading and develop multivariate risk outcome models. Cancer Research. 80(4_Supplement). P3–8.

Donovan, Michael, Gerardo Fernández, Faisal M. Khan, et al.. (2018). Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test. Prostate Cancer and Prostatic Diseases. 21(4). 594–603. 17 indexed citations

Donovan, Michael, et al.. (2018). The application of artificial intelligence and machine learning to automate Gleason grading: Novel tools to develop next generation risk assessment assays.. Journal of Clinical Oncology. 36(6_suppl). 170–170. 2 indexed citations

10.

Khan, Faisal M., et al.. (2016). Pathological Gleason prediction through gland ring morphometry in immunofluorescent prostate cancer images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9791. 97910V–97910V. 1 indexed citations

11.

Khan, Faisal M., et al.. (2014). Gland Ring Morphometry for Prostate Cancer Prognosis in Multispectral Immunofluorescence Images. Lecture notes in computer science. 17(Pt 1). 585–592. 5 indexed citations

12.

Liu, Qiuhua, et al.. (2012). Iterative approach to joint segmentation of cellular structures. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8314. 83140X–83140X. 3 indexed citations

13.

Khan, Faisal M., et al.. (2012). Adaptive epithelial cytoplasm segmentation and epithelial unit separation in immunoflurorescent images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8314. 831427–831427. 1 indexed citations

14.

Marchevsky, Alberto M., Viera Nelson, Sue Martin, et al.. (2003). Telecytology of fine‐needle aspiration biopsies of the pancreas: A study of well‐differentiated adenocarcinoma and chronic pancreatitis with atypical epithelial repair changes. Diagnostic Cytopathology. 28(3). 147–152. 33 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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