Harshita Sharma

1.6k citations
56 papers · 905 indexed · h-index 17

Harshita Sharma

51 papers receiving 884 citations

Peers

Harshita Sharma
Comparison fields: 5 of 106
  • Health Informatics 108
  • General Dentistry 36
  • Artificial Intelligence 487
  • Radiology, Nuclear Medicine and Imaging 344
  • Computer Vision and Pattern Recognition 262
Replace Avinash V. Varadarajan with:
Avinash V. Varadarajan United States
D. R. Sarvamangala India
Ali Abbasian Ardakani Iran
Jaime Melendez Netherlands
Avi Ben-Cohen Israel
Di Xiao Australia
Clifford Yang United States
Sasank Chilamkurthy United States
Edward Korot United Kingdom
Harshita Sharma relative to Avinash V. Varadarajan United States Avinash V. Varadarajan's profile →
Citations per field
00.5×12×
Avinash V. Varadarajan · 1×
Citations per year

Countries citing papers authored by Harshita Sharma

Since Specialization
Citations

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

Fields of papers citing papers by Harshita Sharma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Harshita Sharma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Harshita Sharma Line = papers co-authored together Harshita Sharma links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20244
2 20240
3 20231
4 20236
5 202314
6 20231
7 20230
8 20235
9 20236
10 20222
11 202214
12 202112
13 20215
14 202133
15 202147
16 201919
17
Evaluation of gaze tracking calibration for longitudinal biomedical imaging studies
20181
18 201823
19 20176
20 20162

About Harshita Sharma

Harshita Sharma is a scholar working on Health Informatics, Microbiology and Computer Vision and Pattern Recognition, having authored 56 papers that have together received 905 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (14 papers), Fetal and Pediatric Neurological Disorders (14 papers), Multimodal Machine Learning Applications (12 papers), AI in cancer detection (9 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Topic Modeling (5 papers), Image Retrieval and Classification Techniques (5 papers) and Neonatal and fetal brain pathology (5 papers). The work is most often cited by research in Health Informatics (108 citations), General Dentistry (36 citations) and Artificial Intelligence (487 citations). Harshita Sharma has collaborated with scholars based in United Kingdom, India and Germany. Frequent co-authors include Peter Hufnagl, Olaf Hellwich, Norman Zerbe, J. Alison Noble, Lior Drukker, Aris T. Papageorghiou, Pierre Chatelain, Richard Droste, Yifan Cai and Arathi Rao. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and PLoS ONE.

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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