Siddhi Ramesh

1.4k total citations · 1 hit paper
11 papers, 537 citations indexed

About

Siddhi Ramesh is a scholar working on Artificial Intelligence, Cancer Research and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Siddhi Ramesh has authored 11 papers receiving a total of 537 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 5 papers in Cancer Research and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Siddhi Ramesh's work include AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Cancer Genomics and Diagnostics (4 papers). Siddhi Ramesh is often cited by papers focused on AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Cancer Genomics and Diagnostics (4 papers). Siddhi Ramesh collaborates with scholars based in United States, Germany and India. Siddhi Ramesh's co-authors include Alexander T. Pearson, Emma Dyer, Frederick M. Howard, Catherine A. Gao, Yuan Luo, Nikolay S. Markov, James M. Dolezal, Sara Kochanny, Andrew Srisuwananukorn and Aaron S. Mansfield and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and Science Advances.

In The Last Decade

Siddhi Ramesh

11 papers receiving 522 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers

2023 373 citations Catherine A. Gao, Frederick M. Howard et al. npj Digital Medicine profile →

Peers

Siddhi Ramesh

HI

AI

RNMI

SR

PHEOH

Emma Dyer United States

Helen Frazer Australia

Sophia J. Wagner Germany

Pranavsingh Dhunnoo Ireland

Michaela Unger Germany

Nicolás Nieto Argentina

Agostina J. Larrazabal Argentina

Kevin Wu United States

Hannah Sophie Muti Germany

Kevin Faust Canada

Emma Dyer United States

Siddhi Ramesh

299 ×1.0

HI

190 ×0.8

AI

134 ×0.8

RNMI

47 ×1.0

SR

31 ×1.1

PHEOH

Citations per year, relative to Siddhi Ramesh Siddhi Ramesh (= 1×) peers Emma Dyer

Countries citing papers authored by Siddhi Ramesh

Since Specialization

Citations

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

Fields of papers citing papers by Siddhi Ramesh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Siddhi Ramesh

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

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Ramesh, Siddhi, Teague Tomesh, Samantha J. Riesenfeld, Frederic T. Chong, & Alexander T. Pearson. (2024). Quantum computing for oncology. Nature Cancer. 5(6). 811–816. 2 indexed citations

2.

Howard, Frederick M., Siddhi Ramesh, James M. Dolezal, et al.. (2024). Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features. Science Advances. 10(46). eadq0856–eadq0856. 8 indexed citations

3.

Dolezal, James M., Sara Kochanny, Emma Dyer, et al.. (2024). Slideflow: deep learning for digital histopathology with real-time whole-slide visualization. BMC Bioinformatics. 25(1). 134–134. 20 indexed citations

4.

Dolezal, James M., Emma Dyer, Sara Kochanny, et al.. (2024). Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning. EBioMedicine. 107. 105276–105276. 5 indexed citations

5.

Saldanha, Oliver Lester, Chiara Maria Lavinia Loeffler, Jan Niehues, et al.. (2023). Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology. npj Precision Oncology. 7(1). 35–35. 41 indexed citations

6.

Gao, Catherine A., Frederick M. Howard, Nikolay S. Markov, et al.. (2023). Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. npj Digital Medicine. 6(1). 75–75. 373 indexed citations breakdown →

7.

Ramesh, Siddhi, James M. Dolezal, Sara Kochanny, et al.. (2023). Artificial intelligence-based cancer progression prediction of oral premalignant lesions via self-supervised deep learning on histopathology.. JCO Global Oncology. 9(Supplement_1). 90–90. 2 indexed citations

8.

Dolezal, James M., Andrew Srisuwananukorn, Dmitry Karpeyev, et al.. (2022). Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology. Nature Communications. 13(1). 6572–6572. 75 indexed citations

9.

Ramesh, Siddhi, James M. Dolezal, & Alexander T. Pearson. (2022). Applications of Deep Learning in Endocrine Neoplasms. Surgical pathology clinics. 16(1). 167–176. 2 indexed citations

10.

Mayampurath, Anoop, Siddhi Ramesh, Meaghan Granger, et al.. (2021). Predicting Response to Chemotherapy in Patients With Newly Diagnosed High-Risk Neuroblastoma: A Report From the International Neuroblastoma Risk Group. JCO Clinical Cancer Informatics. 5(5). 1181–1188. 8 indexed citations

11.

Ramesh, Siddhi, Meaghan Granger, Arlene Naranjo, et al.. (2021). Predicting response to chemotherapy in neuroblastoma using deep learning: A report from the International Neuroblastoma Risk Group.. Journal of Clinical Oncology. 39(15_suppl). 10039–10039. 1 indexed citations

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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