Maya Mor
Impact in
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- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Pathology and Forensic Medicine top 10%
- Lymphoma Diagnosis and Treatment
Papers in ⓘ
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- Renal Transplantation Outcomes and Treatments 2
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- Lymphoma Diagnosis and Treatment 5
- Co-authors
- Rachel Bar‐Shalom (7 shared papers)Diana Gaitini (6 shared papers)Ora Israel (6 shared papers)Ron Epelbaum (5 shared papers)Alex Frenkel (5 shared papers)Dov Front (4 shared papers)Nissim Haim (4 shared papers)Gerald M. Kolodny (4 shared papers)
- Journals
- Cancer (2 papers)Radiology (2 papers)Seminars in Nuclear Medicine (1 paper)Journal of Clinical Medicine (1 paper)Pediatric Nephrology (1 paper)
- Partner nations
- IsraelUnited States
In The Last Decade
Maya Mor
11 papers receiving 283 citations
Peers
Comparison fields: 5 of 45
- Radiology, Nuclear Medicine and Imaging 210
- Pathology and Forensic Medicine 137
- Transplantation 14
- Pulmonary and Respiratory Medicine 114
- Neurology 30
Countries citing papers authored by Maya Mor
This map shows the geographic impact of Maya Mor'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 Maya Mor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Mor more than expected).
Fields of papers citing papers by Maya Mor
This network shows the impact of papers produced by Maya Mor. 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 Maya Mor. The network helps show where Maya Mor may publish in the future.
Co-authors
The 25 scholars most cited alongside Maya Mor, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2000 | 57 | |
| 2 | 1999 | 50 | |
| 3 | 2001 | 46 | |
| 4 | Is 18F-FDG PET/CT useful for imaging and management of patients with suspected occult recurrence of cancer? | 2004 | 45 |
| 5 | Combined functional and structural evaluation of cancer patients with a hybrid camera-based PET/CT system using (18)F-FDG. | 2002 | 42 |
| 6 | 2002 | 18 | |
| 7 | 2016 | 14 | |
| 8 | 2001 | 8 | |
| 9 | 2021 | 6 | |
| 10 | 2004 | 3 | |
| 11 | 2002 | 1 | |
| 12 | 2023 | 0 |
About Maya Mor
Maya Mor is a scholar working on Transplantation, Pathology and Forensic Medicine, Internal Medicine, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine, having authored 12 papers that have together received 290 indexed citations. Recurring topics across this work include Lymphoma Diagnosis and Treatment (5 papers), Medical Imaging Techniques and Applications (5 papers), Medical Imaging and Pathology Studies (3 papers), Lung Cancer Treatments and Mutations (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Renal Transplantation Outcomes and Treatments (2 papers), Esophageal Cancer Research and Treatment (2 papers) and Chronic Kidney Disease and Diabetes (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (210 citations), Pathology and Forensic Medicine (137 citations), Transplantation (14 citations), Pulmonary and Respiratory Medicine (114 citations) and Neurology (30 citations). Maya Mor has collaborated with scholars based in Israel and United States. Frequent co-authors include Rachel Bar‐Shalom, Diana Gaitini, Ora Israel, Ron Epelbaum, Alex Frenkel, Dov Front, Nissim Haim, Gerald M. Kolodny, Stanley J. Goldsmith and Zohar Keidar. Their work appears in journals such as Cancer, Radiology, Seminars in Nuclear Medicine, Journal of Clinical Medicine and Pediatric Nephrology.
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.