Maha Shady
Impact in
- Health Informatics top 5%
- Cancer Research top 10%
- Cancer Genomics and Diagnostics
Papers in
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- Cancer Genomics and Diagnostics 3
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- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- Ming Y. Lu (6 shared papers)Tiffany Chen (6 shared papers)Faisal Mahmood (5 shared papers)Richard J. Chen (4 shared papers)Drew F. K. Williamson (4 shared papers)Jana Lipková (5 shared papers)Mane Williams (4 shared papers)Muhammad Shaban (2 shared papers)
- Journals
- Nature Medicine (2 papers)Journal of Pathology Informatics (2 papers)Journal of the American College of Cardiology (1 paper)Cancer Cell (1 paper)QJM (1 paper)
- Partner nations
- United StatesSwitzerlandTürkiye
In The Last Decade
Maha Shady
11 papers receiving 1.4k citations
Maha Shady's Hit Papers
Peers
Comparison fields: 5 of 79
- Health Informatics 59
- Cancer Research 263
- Oncology 431
- Radiology, Nuclear Medicine and Imaging 321
- Artificial Intelligence 448
Countries citing papers authored by Maha Shady
This map shows the geographic impact of Maha Shady'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 Maha Shady with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maha Shady more than expected).
Fields of papers citing papers by Maha Shady
This network shows the impact of papers produced by Maha Shady. 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 Maha Shady. The network helps show where Maha Shady may publish in the future.
Co-authors
The 24 scholars most cited alongside Maha Shady, 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 | A rectal cancer organoid platform to study individual responses to chemoradiation Hit paper breakdown → | 2019 | 423 |
| 2 | Pan-cancer integrative histology-genomic analysis via multimodal deep learning Hit paper breakdown → | 2022 | 395 |
| 3 | Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images Hit paper breakdown → | 2021 | 228 |
| 4 | 2018 | 107 | |
| 5 | 2019 | 87 | |
| 6 | 2022 | 71 | |
| 7 | 2021 | 45 | |
| 8 | 2022 | 13 | |
| 9 | 2022 | 7 | |
| 10 | 2024 | 3 | |
| 11 | 2021 | 1 |
About Maha Shady
Maha Shady is a scholar working on Cancer Research, Radiology, Nuclear Medicine and Imaging, Oncology, Artificial Intelligence and Surgery, having authored 11 papers that have together received 1.4k indexed citations. Recurring topics across this work include Cancer Genomics and Diagnostics (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), AI in cancer detection (3 papers), Cancer Diagnosis and Treatment (2 papers), Transplantation: Methods and Outcomes (2 papers), Medical Imaging and Pathology Studies (1 paper), Infectious Disease Case Reports and Treatments (1 paper) and Cancer Cells and Metastasis (1 paper). The work is most often cited by research in Health Informatics (59 citations), Cancer Research (263 citations), Oncology (431 citations), Radiology, Nuclear Medicine and Imaging (321 citations) and Artificial Intelligence (448 citations). Maha Shady has collaborated with scholars based in United States, Switzerland and Türkiye. Frequent co-authors include Ming Y. Lu, Tiffany Chen, Faisal Mahmood, Richard J. Chen, Drew F. K. Williamson, Jana Lipková, Mane Williams, Muhammad Shaban, Wei‐Hung Weng and Trevor Manz. Their work appears in journals such as Nature Medicine, Journal of Pathology Informatics, Journal of the American College of Cardiology, Cancer Cell and QJM.
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.