Manish Sapkota

653 citations
10 papers · 392 indexed · h-index 6
Topics
AI in cancer detection (4 papers)Advanced Image and Video Retrieval Techniques (4 papers)Radiomics and Machine Learning in Medical Imaging (1 paper)
Partner nations
United StatesChina

In The Last Decade

Manish Sapkota

10 papers receiving 388 citations

Peers

Manish Sapkota
Comparison fields: 5 of 63
  • Artificial Intelligence 258
  • Computer Vision and Pattern Recognition 205
  • Radiology, Nuclear Medicine and Imaging 126
  • Oncology 43
  • Biophysics 38
Replace Michael Gadermayr with:
Michael Gadermayr Austria
Xiaofei Luo Japan
Shahira Abousamra United States
Quoc Dang Vu United Kingdom
Ozan Ciga Canada
Bruno Korbar United States
Baochuan Pang China
Hammad Qureshi Pakistan
Ruining Deng United States
Yanda Meng United Kingdom
Manish Sapkota relative to Michael Gadermayr Austria Michael Gadermayr's profile →
Citations per field
00.5×2.8×
Michael Gadermayr · 1×
Citations per year

Countries citing papers authored by Manish Sapkota

Since Specialization
Citations

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

Fields of papers citing papers by Manish Sapkota

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manish Sapkota

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

All Works

10 of 10 papers shown
#WorkIndexed citations
1 9
2 2
3 189
4 3
5 27
6 67
7 40
8 2
9 48
10 5

About Manish Sapkota

Manish Sapkota is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Artificial Intelligence, having authored 10 papers that have together received 392 indexed citations. Recurring topics across this work include AI in cancer detection (4 papers), Advanced Image and Video Retrieval Techniques (4 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). The work is most often cited by research in Health Informatics (18 citations), Computer Vision and Pattern Recognition (205 citations) and Artificial Intelligence (258 citations). Manish Sapkota has collaborated with scholars based in United States and China. Frequent co-authors include Lin Yang, Fuyong Xing, Xiaoshuang Shi, Zizhao Zhang, Yuanpu Xie, Fujun Liu, Hai Su, Shohreh Dickinson, Mason McGough and Jinzheng Cai. Their work appears in journals such as IEEE Transactions on Image Processing, Pattern Recognition and IEEE Journal of Biomedical and Health Informatics.

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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