Jayasree Chakraborty
- Radiology, Nuclear Medicine and Imaging top 1%
- Oncology top 5%
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Pulmonary and Respiratory Medicine top 10%
- Co-authors
- Abhishek MidyaAmber L. SimpsonRichard Kinh GianMithat GönenPeter J. AllenSudipta MukhopadhyayWilliam R. JarnaginLiana Langdon‐Embry
- Topics
- Radiomics and Machine Learning in Medical Imaging (41 papers)AI in cancer detection (27 papers)Pancreatic and Hepatic Oncology Research (21 papers)
- Partner nations
- United StatesIndiaCanada
In The Last Decade
Jayasree Chakraborty
77 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 87
- Radiology, Nuclear Medicine and Imaging 911
- Oncology 544
- Artificial Intelligence 508
- Computer Vision and Pattern Recognition 369
- Pulmonary and Respiratory Medicine 268
Countries citing papers authored by Jayasree Chakraborty
This map shows the geographic impact of Jayasree Chakraborty'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 Jayasree Chakraborty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jayasree Chakraborty more than expected).
Fields of papers citing papers by Jayasree Chakraborty
This network shows the impact of papers produced by Jayasree Chakraborty. 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 Jayasree Chakraborty. The network helps show where Jayasree Chakraborty may publish in the future.
Co-authorship network of co-authors of Jayasree Chakraborty
This figure shows the co-authorship network connecting the top 25 collaborators of Jayasree Chakraborty. A scholar is included among the top collaborators of Jayasree Chakraborty 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 Jayasree Chakraborty. Jayasree Chakraborty 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 | 0 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 14 | |
| 6 | 17 | |
| 7 | 5 | |
| 8 | 12 | |
| 9 | 55 | |
| 10 | 79 | |
| 11 | 39 | |
| 12 | 8 | |
| 13 | 12 | |
| 14 | 43 | |
| 15 | 21 | |
| 16 | 89 | |
| 17 | 1 | |
| 18 | 13 | |
| 19 | 9 | |
| 20 | A study on heterobimetallic chemistry of polyfunctional bis(2-hydroxy-1-naphthaldehyde)malonoyldihydrazone: Dioxouranium(VI), dioxomolybdenum(VI), zinc(II), copper(II), nickel(II) and cobalt(II) complexes | 1 |
About Jayasree Chakraborty
Jayasree Chakraborty is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Hepatology, having authored 86 papers that have together received 1.5k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (41 papers), AI in cancer detection (27 papers) and Pancreatic and Hepatic Oncology Research (21 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (911 citations), Hepatology (159 citations) and Oncology (544 citations). Jayasree Chakraborty has collaborated with scholars based in United States, India and Canada. Frequent co-authors include Abhishek Midya, Amber L. Simpson, Richard Kinh Gian, Mithat Gönen, Peter J. Allen, Sudipta Mukhopadhyay, William R. Jarnagin, Liana Langdon‐Embry, Amit Konar and Uday K. Chakraborty. Their work appears in journals such as Journal of Clinical Oncology, PLoS ONE and Cancer Research.
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.