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Neeraj Kumar
Neeraj Kumar
Alberta Machine Intelligence Institute
Verified email at iitg.ernet.in - Homepage
Title
Cited by
Cited by
Year
A dataset and a technique for generalized nuclear segmentation for computational pathology
N Kumar, R Verma, S Sharma, S Bhargava, A Vahadane, A Sethi
IEEE transactions on medical imaging 36 (7), 1550-1560, 2017
8532017
A multi-organ nucleus segmentation challenge
N Kumar, R Verma, D Anand, Y Zhou, OF Onder, E Tsougenis, H Chen, ...
IEEE transactions on medical imaging 39 (5), 1380-1391, 2019
3602019
Federated learning enables big data for rare cancer boundary detection
S Pati, U Baid, B Edwards, M Sheller, SH Wang, GA Reina, P Foley, ...
Nature communications 13 (1), 7346, 2022
1192022
MoNuSAC2020: A multi-organ nuclei segmentation and classification challenge
R Verma, N Kumar, A Patil, NC Kurian, S Rane, S Graham, QD Vu, ...
IEEE Transactions on Medical Imaging 40 (12), 3413-3423, 2021
1092021
Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images
A Sethi, L Sha, AR Vahadane, RJ Deaton, N Kumar, V Macias, PH Gann
Journal of pathology informatics 7 (1), 17, 2016
652016
Convolutional neural networks for wavelet domain super resolution
N Kumar, R Verma, A Sethi
Pattern Recognition Letters 90, 65-71, 2017
602017
Convolutional neural networks for prostate cancer recurrence prediction
N Kumar, R Verma, A Arora, A Kumar, S Gupta, A Sethi, PH Gann
Medical Imaging 2017: Digital Pathology 10140, 106-117, 2017
562017
Systems and methods for computational pathology using points-of-interest
A Sethi, N Kumar
US Patent 10,573,003, 2020
512020
Fast learning-based single image super-resolution
N Kumar, A Sethi
IEEE Transactions on Multimedia 18 (8), 1504-1515, 2016
482016
Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images
D Anand, NC Kurian, S Dhage, N Kumar, S Rane, PH Gann, A Sethi
Journal of pathology informatics 11 (1), 19, 2020
422020
Hyperspectral tissue image segmentation using semi-supervised NMF and hierarchical clustering
N Kumar, P Uppala, K Duddu, H Sreedhar, V Varma, G Guzman, M Walsh, ...
IEEE transactions on medical imaging 38 (5), 1304-1313, 2018
382018
Weakly supervised learning on unannotated H&E‐stained slides predicts BRAF mutation in thyroid cancer with high accuracy
D Anand, K Yashashwi, N Kumar, S Rane, PH Gann, A Sethi
The Journal of pathology 255 (3), 232-242, 2021
372021
Multi-organ nuclei segmentation and classification challenge 2020
R Verma, N Kumar, A Patil, NC Kurian, S Rane, A Sethi
IEEE transactions on medical imaging 39 (1380-1391), 8, 2020
322020
Detecting multiple sub-types of breast cancer in a single patient
ASPHG Ruchika Verma, Neeraj Kumar
IEEE International Conference on Image Processing (ICIP), 2016
162016
Quantification of intrinsic subtype ambiguity in Luminal A breast cancer and its relationship to clinical outcomes
N Kumar, D Zhao, D Bhaumik, PH Sethi, Amit and Gann
BMC Cancer 19 (215), 2019
142019
Neural network based image deblurring
N Kumar, R Nallamothu, A Sethi
11th Symposium on Neural Network Applications in Electrical Engineering, 219-222, 2012
142012
Learning to predict super resolution wavelet coefficients
N Kumar, NK Rai, A Sethi
Proceedings of the 21st International Conference on Pattern Recognition …, 2012
132012
Neural network based single image super resolution
N Kumar, PK Deswal, J Mehta, A Sethi
11th Symposium on Neural Network Applications in Electrical Engineering, 213-218, 2012
112012
Uncertainty estimation in cancer survival prediction
H Loya, P Poduval, D Anand, N Kumar, A Sethi
arXiv preprint arXiv:2003.08573, 2020
82020
Super resolution by comprehensively exploiting dependencies of wavelet coefficients
N Kumar, A Sethi
IEEE Transactions on Multimedia 20 (2), 298-309, 2017
82017
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Articles 1–20