A Coarse Grained Reconfigurable Architecture for Variable Block Size Motion Estimation. - Ruchika Verma; Ali Akoglu (FPT, 2007)

A coarse grained and hybrid reconfigurable architecture with flexible NoC router for variable block size motion estimation. - Ruchika Verma; Ali Akoglu (IPDPS, 2008)

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

Radiogenomic characterization of response to chemo-radiation therapy in glioblastoma is associated with PI3K/AKT/mTOR and apoptosis signaling pathways. - Niha G. Beig; Prateek Prasanna; Virginia B. Hill; Ruchika Verma; Vinay Varadan; Anant Madabhushi; Pallavi Tiwari (Computer-Aided Diagnosis, 2019)

Deformation heterogeneity radiomics to predict molecular subtypes of pediatric Medulloblastoma on routine MRI. - Sukanya Iyer; Marwa Ismail; Benita Tamrazi; Ashley Margol; Ruchika Verma; Ramon Correa; Prateek Prasanna; Niha G. Beig; Kaustav Bera; Volodymyr Statsevych; Alexander R. Judkins; Anant Madabhushi; et al. (Computer-Aided Diagnosis, 2019)

Radiomics of the lesion habitat on pre-treatment MRI predicts response to chemo-radiation therapy in Glioblastoma. - Ruchika Verma; Ramon Correa; Virginia B. Hill; Niha G. Beig; Abdelkader Mahammedi; Anant Madabhushi; Pallavi Tiwari (Computer-Aided Diagnosis, 2019)

Combining deep and hand-crafted MRI features for identifying sex-specific differences in autism spectrum disorder versus controls. - Yashas Hiremath; Marwa Ismail; Ruchika Verma; Jacob Antunes; Pallavi Tiwari (Computer-Aided Diagnosis, 2020)

Spatial-And-Context Aware (SpACe) "Virtual Biopsy" Radiogenomic Maps to Target Tumor Mutational Status on Structural MRI. - Marwa Ismail; Ramon Correa; Kaustav Bera; Ruchika Verma; Anas Saeed Bamashmos; Niha G. Beig; Jacob Antunes; Prateek Prasanna; Volodymyr Statsevych; Manmeet Ahluwalia; Pallavi Tiwari (MICCAI (2), 2020)

MRQy: An Open-Source Tool for Quality Control of MR Imaging Data. - Amir Reza Sadri; Andrew Janowczyk; Ren Zou; Ruchika Verma; Jacob Antunes; Anant Madabhushi; Pallavi Tiwari; Satish E. Viswanath (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)

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)

A hierarchical deep learning approach for segmentation of glioblastoma tumor niches on digital histopathology. - Alvaro Sandino Garzon; Ruchika Verma; Yijang Chen; David Becerra Tovar; Eduardo Romero Castro; Pallavi Tiwari (Digital and Computational Pathology, 2022)

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)

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)

A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models. - Gabriele Campanella; Shengjia Chen; Ruchika Verma; Jennifer Zeng; Aryeh Stock; Matt Croken; Brandon Veremis; Abdulkadir Elmas; Kuan-Lin Huang; Ricky Kwan; Jane Houldsworth; Adam Schoenfeld; et al. (CoRR, 2024)

Artificial intelligence and machine learning applications for cultured meat. - Michael E. Todhunter; Sheikh Jubair; Ruchika Verma; Rikard Saqe; Kevin Shen; Breanna Duffy (Frontiers Artif. Intell., 2024)

Predicting Patient Self-reported Race From Skin Histological Images with Deep Learning. - Shengjia Chen; Ruchika Verma; Kevin Clare; Jannes Jegminat; Eugenia Alleva; Kuan-Lin Huang; Brandon Veremis; Thomas J. Fuchs; Gabriele Campanella (FAIMI@MICCAI, 2025)

Predict Patient Self-reported Race from Skin Histological Images. - Shengjia Chen; Ruchika Verma; Kevin Clare; Jannes Jegminat; Eugenia Alleva; Kuan-Lin Huang; Brandon Veremis; Thomas J. Fuchs; Gabriele Campanella (CoRR, 2025)