Sandra Jardim
Impact in
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- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Health Information Management top 10%
Papers in
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- Medical Image Segmentation Techniques 6
- Image Retrieval and Classification Techniques 3
- Advanced Neural Network Applications 3
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- Sentiment Analysis and Opinion Mining 2
- AI in cancer detection 2
- Domain Adaptation and Few-Shot Learning 2
- Co-authors
- Mário A. T. Figueiredo (2 shared papers)Nuno Cardoso (1 shared paper)Joaquim Jorge (1 shared paper)Catarina Moreira (1 shared paper)João Pereira (1 shared paper)
In The Last Decade
Sandra Jardim
14 papers receiving 262 citations
Peers
Comparison fields: 5 of 100
- Computer Vision and Pattern Recognition 86
- Health Information Management 15
- Health Informatics 4
- Artificial Intelligence 90
- Pediatrics, Perinatology and Child Health 48
Countries citing papers authored by Sandra Jardim
This map shows the geographic impact of Sandra Jardim'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 Sandra Jardim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sandra Jardim more than expected).
Fields of papers citing papers by Sandra Jardim
This network shows the impact of papers produced by Sandra Jardim. 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 Sandra Jardim. The network helps show where Sandra Jardim may publish in the future.
Co-authors
The 5 scholars most cited alongside Sandra Jardim, 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 | 2005 | 67 | |
| 2 | 2023 | 51 | |
| 3 | 2023 | 40 | |
| 4 | 2013 | 35 | |
| 5 | 2022 | 26 | |
| 6 | 2022 | 22 | |
| 7 | 2004 | 15 | |
| 8 | 2022 | 8 | |
| 9 | 2023 | 2 | |
| 10 | 2018 | 2 | |
| 11 | 2025 | 2 | |
| 12 | 2024 | 1 | |
| 13 | 2024 | 1 | |
| 14 | 2021 | 1 | |
| 15 | 2025 | 0 | |
| 16 | 2025 | 0 | |
| 17 | 2021 | 0 |
About Sandra Jardim
Sandra Jardim is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Sociology and Political Science, Radiology, Nuclear Medicine and Imaging and Human-Computer Interaction, having authored 17 papers that have together received 273 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (6 papers), Image Retrieval and Classification Techniques (3 papers), Digital Marketing and Social Media (3 papers), Advanced Neural Network Applications (3 papers), COVID-19 diagnosis using AI (2 papers), Sentiment Analysis and Opinion Mining (2 papers), AI in cancer detection (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (86 citations), Health Information Management (15 citations), Health Informatics (4 citations), Artificial Intelligence (90 citations) and Pediatrics, Perinatology and Child Health (48 citations). Sandra Jardim has collaborated with scholars based in Portugal, Mexico and Australia. Frequent co-authors include Mário A. T. Figueiredo, Nuno Cardoso, Joaquim Jorge, Catarina Moreira and João Pereira. Their work appears in journals such as Smart Cities, PLoS ONE, Ultrasound in Medicine & Biology, Journal of Imaging and SN Computer Science.
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.