Neeraj Kumar

1.9k total citations · 1 hit paper
27 papers, 967 citations indexed

About

Neeraj Kumar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Neeraj Kumar has authored 27 papers receiving a total of 967 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 10 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Neeraj Kumar's work include Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (8 papers) and Advanced Image Processing Techniques (8 papers). Neeraj Kumar is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (8 papers) and Advanced Image Processing Techniques (8 papers). Neeraj Kumar collaborates with scholars based in India, United States and Canada. Neeraj Kumar's co-authors include Amit Sethi, Ruchika Verma, Abhishek Vahadane, Peter H. Gann, Deepak Anand, Swapnil Rane, Virgilia Macias, Ryan Deaton, Abhay Kumar and Michael J. Walsh and has published in prestigious journals such as PLoS ONE, Cancer Research and Scientific Reports.

In The Last Decade

Neeraj Kumar

26 papers receiving 943 citations

Hit Papers

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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.

A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology

2017 671 citations Neeraj Kumar, Ruchika Verma et al. IEEE Transactions on Medical Imaging profile →

Peers

Countries citing papers authored by Neeraj Kumar

Since Specialization

Citations

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

Fields of papers citing papers by Neeraj Kumar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Neeraj Kumar

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

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images 2024 ·Cancer Research Communications ·(unknown), Peter H. Gann, Neeraj Kumar, Stephanie M. McGregor, Ruchika Verma, Amit Sethi	2
2	Accurate personalized survival prediction for amyotrophic lateral sclerosis patients 2023 ·Scientific Reports ·(unknown), (unknown), Rafsanjany Kushol, Neeraj Kumar, (unknown), Sanjay Kalra, Russell Greiner	6
3	Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction 2023 ·IEEE Transactions on Biomedical Engineering ·Shi-ang Qi, Neeraj Kumar, Ruchika Verma, Jianyi Xu, Grace Shen‐Tu, Russell Greiner	2
4	Learning Individual Survival Models from PanCancer Whole Transcriptome Data 2023 ·Clinical Cancer Research ·Neeraj Kumar, Daniel Skubleny, Michael Parkes, Ruchika Verma, S. Davis, (unknown), (unknown), Russell Greiner	1
5	Learning to predict prostate cancer recurrence from tissue images 2023 ·Journal of Pathology Informatics ·(unknown), Neeraj Kumar, Peter H. Gann, Russell Greiner	2
6	Learning accurate personalized survival models for predicting hospital discharge and mortality of COVID-19 patients 2022 ·Scientific Reports ·Neeraj Kumar, Shi-ang Qi, (unknown), (unknown), (unknown), Russell Greiner	6
7	Personalized breast cancer onset prediction from lifestyle and health history information 2022 ·PLoS ONE ·Shi-ang Qi, Neeraj Kumar, Jianyi Xu, Jaykumar Patel, (unknown), Grace Shen‐Tu, Russell Greiner	5
8	Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors 2022 ·The Journal of Pathology ·Neeraj Kumar, Ruchika Verma, (unknown), Cheng Lu, Pingfu Fu, Joseph Willis, Anant Madabhushi	6
9	A comprehensive survey on aspect-based sentiment analysis 2021 ·International Journal of Engineering Systems Modelling and Simulation ·Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, Neeraj Kumar, (unknown)	2
10	Weakly supervised learning on unannotated H&E‐stained slides predicts BRAF mutation in thyroid cancer with high accuracy 2021 ·The Journal of Pathology ·Deepak Anand, (unknown), Neeraj Kumar, Swapnil Rane, Peter H. Gann, Amit Sethi	32
11	Abstract 3: Deep learning-based integration of esophageal cancer morphology with genomics 2021 ·Cancer Research ·Ruchika Verma, Wei Wu, Neeraj Kumar, Elizabeth A. Yu, Won‐Tak Choi, Sarah E. Umetsu, Trever G. Bivona	1
12	Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images 2020 ·Journal of Pathology Informatics ·Deepak Anand, (unknown), (unknown), Neeraj Kumar, Swapnil Rane, Peter H. Gann, Amit Sethi	41
13	A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology breakdown → 2017 ·IEEE Transactions on Medical Imaging ·Neeraj Kumar, Ruchika Verma, (unknown), (unknown), Abhishek Vahadane, Amit Sethi	671
14	Convolutional neural networks for wavelet domain super resolution 2017 ·Pattern Recognition Letters ·Neeraj Kumar, Ruchika Verma, Amit Sethi	37
15	Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images 2016 ·Journal of Pathology Informatics ·Amit Sethi, (unknown), Abhishek Vahadane, Ryan Deaton, Neeraj Kumar, Virgilia Macias, Peter H. Gann	38
16	Learning based super-resolution of histological images 2016 ·Abhishek Vahadane, Neeraj Kumar, Amit Sethi	4
17	A spatial neighbourhood based learning setup for super resolution 2012 ·Amit Sethi, Neeraj Kumar, (unknown)	0
18	Neural network based single image super resolution 2012 ·Neeraj Kumar, (unknown), (unknown), Amit Sethi	3
19	On image-driven choice of wavelet basis for super resolution 2012 ·Neeraj Kumar, Amit Sethi	2
20	Neural network based image deblurring 2012 ·Neeraj Kumar, (unknown), Amit Sethi	6

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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