Shokofeh Anari
- Computer Vision and Pattern Recognition top 5%
- Neurology top 5%
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Biomedical Engineering
- Co-authors
- Ramin RanjbarzadehMalika BendechacheSaeid Jafarzadeh GhoushchiMaryam NaseriN. SarsharShadi DorostiNavid RazmjooyYaghoub Pourasad
- Topics
- Brain Tumor Detection and Classification (5 papers)AI in cancer detection (5 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)
In The Last Decade
Shokofeh Anari
9 papers receiving 616 citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Computer Vision and Pattern Recognition 324
- Neurology 273
- Artificial Intelligence 213
- Radiology, Nuclear Medicine and Imaging 201
- Biomedical Engineering 72
Countries citing papers authored by Shokofeh Anari
This map shows the geographic impact of Shokofeh Anari'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 Shokofeh Anari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shokofeh Anari more than expected).
Fields of papers citing papers by Shokofeh Anari
This network shows the impact of papers produced by Shokofeh Anari. 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 Shokofeh Anari. The network helps show where Shokofeh Anari may publish in the future.
Co-authorship network of co-authors of Shokofeh Anari
This figure shows the co-authorship network connecting the top 25 collaborators of Shokofeh Anari. A scholar is included among the top collaborators of Shokofeh Anari 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 Shokofeh Anari. Shokofeh Anari is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet modelsbreakdown → | 22 |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 5 | |
| 6 | 10 | |
| 7 | 3 | |
| 8 | 78 | |
| 9 | 51 | |
| 10 | 40 | |
| 11 | 54 | |
| 12 | Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain imagesbreakdown → | 377 |
About Shokofeh Anari
Shokofeh Anari is a scholar working on Health Informatics, Neurology and Computer Vision and Pattern Recognition, having authored 12 papers that have together received 640 indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (5 papers), AI in cancer detection (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). The work is most often cited by research in Neurology (273 citations), Computer Vision and Pattern Recognition (324 citations) and Health Informatics (16 citations). Shokofeh Anari has collaborated with scholars based in Ireland, Iran and Türkiye. Frequent co-authors include Ramin Ranjbarzadeh, Malika Bendechache, Saeid Jafarzadeh Ghoushchi, Maryam Naseri, N. Sarshar, Shadi Dorosti, Navid Razmjooy, Yaghoub Pourasad, Erfan Babaee Tırkolaee and Gabriel Gomes de Oliveira. Their work appears in journals such as Scientific Reports, Annals of Operations Research and Mathematical Problems in Engineering.
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.