Nikhil Kumar Tomar

1.2k total citations · 2 hit papers
18 papers, 581 citations indexed

About

Nikhil Kumar Tomar is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Artificial Intelligence. According to data from OpenAlex, Nikhil Kumar Tomar has authored 18 papers receiving a total of 581 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Oncology and 7 papers in Artificial Intelligence. Recurrent topics in Nikhil Kumar Tomar's work include Colorectal Cancer Screening and Detection (8 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and AI in cancer detection (6 papers). Nikhil Kumar Tomar is often cited by papers focused on Colorectal Cancer Screening and Detection (8 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and AI in cancer detection (6 papers). Nikhil Kumar Tomar collaborates with scholars based in United States, United Kingdom and India. Nikhil Kumar Tomar's co-authors include Debesh Jha, Sharib Ali, Jens Rittscher, Dag Johansen, Håvard D. Johansen, Michael A. Riegler, Pål Halvorsen, Ulaş Bağcı, Daniela P. Ladner and Manuel Mazzara and has published in prestigious journals such as Gastroenterology, IEEE Access and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Nikhil Kumar Tomar

17 papers receiving 571 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.

Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning

2021 237 citations Debesh Jha, Sharib Ali et al. IEEE Access profile →
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation

2022 162 citations Nikhil Kumar Tomar, Debesh Jha et al. IEEE Transactions on Neural Networks and Learning Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Nikhil Kumar Tomar United States	8	294	259	232	216	65	18	581
Pia H. Smedsrud Norway	5	181 0.6×	223 0.9×	200 0.9×	228 1.1×	24 0.4×	8	500
Ange Lou United States	7	243 0.8×	157 0.6×	144 0.6×	74 0.3×	42 0.6×	19	429
Shuyue Guan United States	11	232 0.8×	263 1.0×	261 1.1×	70 0.3×	40 0.6×	27	556
Vajira Thambawita Norway	8	152 0.5×	162 0.6×	175 0.8×	156 0.7×	14 0.2×	23	507
Shiliang Ai China	5	160 0.5×	205 0.8×	292 1.3×	52 0.2×	35 0.5×	8	404
Ruikai Zhang China	10	153 0.5×	253 1.0×	249 1.1×	281 1.3×	16 0.2×	16	585
Adrián Colomer Spain	16	293 1.0×	440 1.7×	323 1.4×	114 0.5×	27 0.4×	56	796
Tahir Mahmood South Korea	13	192 0.7×	342 1.3×	232 1.0×	60 0.3×	12 0.2×	30	620
Gopichandh Danala United States	11	88 0.3×	447 1.7×	342 1.5×	54 0.3×	15 0.2×	35	654
Georg Wimmer Austria	11	157 0.5×	142 0.5×	132 0.6×	222 1.0×	14 0.2×	35	454

Countries citing papers authored by Nikhil Kumar Tomar

Since Specialization

Citations

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

Fields of papers citing papers by Nikhil Kumar Tomar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nikhil Kumar Tomar. 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 Nikhil Kumar Tomar. The network helps show where Nikhil Kumar Tomar may publish in the future.

Co-authorship network of co-authors of Nikhil Kumar Tomar

This figure shows the co-authorship network connecting the top 25 collaborators of Nikhil Kumar Tomar. A scholar is included among the top collaborators of Nikhil Kumar Tomar 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 Nikhil Kumar Tomar. Nikhil Kumar Tomar 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:

18 of 18 papers shown

Jha, Debesh, Elif Keleş, Vedat Çiçek, et al.. (2025). A Conceptual Framework for Applying Ethical Principles of AI to Medical Practice. Bioengineering. 12(2). 180–180. 7 indexed citations

Tomar, Nikhil Kumar, et al.. (2025). Vision transformer for efficient chest x-ray and gastrointestinal image classification. 111–111. 2 indexed citations

Tomar, Nikhil Kumar, et al.. (2025). Transformer-Enhanced Iterative Feedback Mechanism For Polyp Segmentation. 1–5. 1 indexed citations

Jha, Debesh, Nikhil Kumar Tomar, Matthew Antalek, et al.. (2025). MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation. 1–5.

Jha, Debesh, et al.. (2025). Optimizing Neural Network Effectiveness via Non-monotonicity Refinement. 4300–4309. 1 indexed citations

Jha, Debesh, Nikhil Kumar Tomar, Matthew Antalek, et al.. (2024). CT Liver Segmentation Via PVT-Based Encoding and Refined Decoding. 1–5. 12 indexed citations

Jha, Debesh, et al.. (2024). TransRUPNet for Improved Polyp Segmentation. PubMed. 2024. 1–4. 1 indexed citations

Jha, Debesh, et al.. (2024). Su1499 ENHANCING LIVER SEGMENTATION OUTCOMES WITH MSFORMERBASED ARTIFICIAL INTELLIGENCE SYSTEM. Gastroenterology. 166(5). S–1617. 1 indexed citations

Tomar, Nikhil Kumar, et al.. (2023). ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes. IEEE Access. 11. 16142–16155. 9 indexed citations

10.

Tomar, Nikhil Kumar, Ulaş Bağcı, & Debesh Jha. (2023). RUPNet: residual upsampling network for real-time polyp segmentation. 54–54. 2 indexed citations

11.

Tomar, Nikhil Kumar, et al.. (2023). TransResU-Net: A Transformer based ResU-Net for Real-Time Colon Polyp Segmentation. PubMed. 2023. 1–4. 24 indexed citations

12.

Tomar, Nikhil Kumar, Debesh Jha, Ulaş Bağcı, & Sharib Ali. (2022). TGANet: Text-Guided Attention for Improved Polyp Segmentation. Lecture notes in computer science. 13433. 151–160. 90 indexed citations

13.

Tomar, Nikhil Kumar, Debesh Jha, Michael A. Riegler, et al.. (2022). FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation. IEEE Transactions on Neural Networks and Learning Systems. 34(11). 9375–9388. 162 indexed citations breakdown →

14.

Tomar, Nikhil Kumar, et al.. (2022). Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network. PubMed. 2022. 323–328. 12 indexed citations

15.

Tomar, Nikhil Kumar, et al.. (2022). Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network. PubMed. 2022. 317–322. 15 indexed citations

16.

Jha, Debesh, Sharib Ali, Nikhil Kumar Tomar, et al.. (2021). Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning. IEEE Access. 9. 40496–40510. 237 indexed citations breakdown →

17.

Tomar, Nikhil Kumar, Nabil Ibtehaz, Debesh Jha, Pål Halvorsen, & Sharib Ali. (2021). Improving Generalizability in Polyp Segmentation using Ensemble Convolutional Neural Network.. 49–58. 2 indexed citations

18.

Ali, Sharib & Nikhil Kumar Tomar. (2021). Iterative deep learning for improved segmentation of endoscopic images. 1(1). 38–40. 3 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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