Learning to predict super resolution wavelet coefficients. - Neeraj Kumar 002; Naveen Kumar Rai; Amit Sethi (ICPR, 2012)

On spatial neighborhood of patch-based super resolution. - Neeraj Kumar 002; Amit Sethi (ICIP, 2015)

Deep Learning-Based Image Kernel for Inductive Transfer. - Neeraj Kumar 002; Ranti Dev Sharma; Animesh Karmakar; Amit Sethi (CoRR, 2015)

Detecting multiple sub-types of breast cancer in a single patient. - Ruchika Verma; Neeraj Kumar 002; Amit Sethi; Peter H. Gann (ICIP, 2016)

Learning based super-resolution of histological images. - Abhishek Vahadane; Neeraj Kumar 002; Amit Sethi (ISBI, 2016)

Fast Learning-Based Single Image Super-Resolution. - Neeraj Kumar 002; Amit Sethi (IEEE Trans. Multim., 2016)

Convolutional neural networks for prostate cancer recurrence prediction. - Neeraj Kumar 002; Ruchika Verma; Ashish Arora; Abhay Kumar; Sanchit Gupta; Amit Sethi; Peter H. Gann (Digital Pathology, 2017)

Convolutional neural networks for wavelet domain super resolution. - Neeraj Kumar 002; Ruchika Verma; Amit Sethi (Pattern Recognit. Lett., 2017)

A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology. - Neeraj Kumar 002; Ruchika Verma; Sanuj Sharma; Surabhi Bhargava; Abhishek Vahadane; Amit Sethi (IEEE Trans. Medical Imaging, 2017)

Super Resolution by Comprehensively Exploiting Dependencies of Wavelet Coefficients. - Neeraj Kumar 002; Amit Sethi (IEEE Trans. Multim., 2018)

Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering. - Neeraj Kumar 002; Phani Krishna Uppala; Karthik Duddu; Hari Sreedhar; Vishal K. Varma; Grace Guzman; Michael J. Walsh 005; Amit Sethi (IEEE Trans. Medical Imaging, 2019)

Uncertainty Estimation in Cancer Survival Prediction. - Hrushikesh Loya; Pranav Poduval; Deepak Anand; Neeraj Kumar 002; Amit Sethi (CoRR, 2020)

A Multi-Organ Nucleus Segmentation Challenge. - Neeraj Kumar 002; Ruchika Verma; Deepak Anand; Yanning Zhou 001; Omer Fahri Onder; Efstratios Tsougenis; Hao Chen 011; Pheng-Ann Heng; Jiahui Li 005; Zhiqiang Hu; Yunzhi Wang; Navid Alemi Koohbanani; et al. (IEEE Trans. Medical Imaging, 2020)

Preface: AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021. - Russell Greiner; Neeraj Kumar 002; Thomas Alexander Gerds; Mihaela van der Schaar (SPACA, 2021)

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge. - Ruchika Verma; Neeraj Kumar 002; Abhijeet Patil; Nikhil Cherian Kurian; Swapnil Rane; Simon Graham; Quoc Dang Vu; Mieke Zwager; Shan-E-Ahmed Raza; Nasir M. Rajpoot; Xiyi Wu; Huai Chen; et al. (IEEE Trans. Medical Imaging, 2021)

Author's Reply to "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge". - Ruchika Verma; Neeraj Kumar 002; Abhijeet Patil; Nikhil Cherian Kurian; Swapnil Rane; Amit Sethi (IEEE Trans. Medical Imaging, 2022)

An Effective Meaningful Way to Evaluate Survival Models. - Shiang Qi; Neeraj Kumar 002; Mahtab Farrokh; Weijie Sun 004; Li-Hao Kuan; Rajesh Ranganath; Ricardo Henao; Russell Greiner (ICML, 2023)

Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction. - Shiang Qi; Neeraj Kumar 002; Ruchika Verma; Jian-Yi Xu; Grace Shen-Tu; Russell Greiner (IEEE Trans. Biomed. Eng., 2023)

The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue. - Philippe Weitz; Masi Valkonen; Leslie Solorzano; Circe Carr; Kimmo Kartasalo; Constance Boissin; Sonja Koivukoski; Aino Kuusela; Dusan Rasic; Yanbo Feng; Sandra Kristiane Sinius Pouplier; Abhinav Sharma; et al. (Medical Image Anal., 2024)