| 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 | 898 | 2017 |
| 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 | 388 | 2019 |
| 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 | 149 | 2022 |
| 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 | 124 | 2021 |
| 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 | 67 | 2016 |
| Convolutional neural networks for wavelet domain super resolution N Kumar, R Verma, A Sethi Pattern Recognition Letters 90, 65-71, 2017 | 60 | 2017 |
| 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 | 57 | 2017 |
| Systems and methods for computational pathology using points-of-interest A Sethi, N Kumar US Patent 10,573,003, 2020 | 55 | 2020 |
| Fast learning-based single image super-resolution N Kumar, A Sethi IEEE Transactions on Multimedia 18 (8), 1504-1515, 2016 | 48 | 2016 |
| 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 | 46 | 2020 |
| 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 | 42 | 2021 |
| 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 | 40 | 2018 |
| 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 | 36 | 2020 |
| Detecting multiple sub-types of breast cancer in a single patient ASPHG Ruchika Verma, Neeraj Kumar IEEE International Conference on Image Processing (ICIP), 2016 | 16 | 2016 |
| 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 | 15 | 2019 |
| Neural network based image deblurring N Kumar, R Nallamothu, A Sethi 11th Symposium on Neural Network Applications in Electrical Engineering, 219-222, 2012 | 15 | 2012 |
| Learning to predict super resolution wavelet coefficients N Kumar, NK Rai, A Sethi Proceedings of the 21st International Conference on Pattern Recognition …, 2012 | 13 | 2012 |
| 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 | 11 | 2012 |
| An effective meaningful way to evaluate survival models S Qi, N Kumar, M Farrokh, W Sun, LH Kuan, R Ranganath, R Henao, ... arXiv preprint arXiv:2306.01196, 2023 | 8 | 2023 |
| The ACROBAT 2022 challenge: automatic registration of breast cancer tissue P Weitz, M Valkonen, L Solorzano, C Carr, K Kartasalo, C Boissin, ... arXiv preprint arXiv:2305.18033, 2023 | 8 | 2023 |
Amit SethiIndian Institute of Technology Bombay, Indian Institute of Technology Guwahati, University of在 iitb.ac.in 的电子邮件经过验证
