Search Results - "Verma, Ruchika"

A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology by Kumar, Neeraj, Verma, Ruchika, Sharma, Sanuj, Bhargava, Surabhi, Vahadane, Abhishek, Sethi, Amit

“…Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in…”
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MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge by Verma, Ruchika, Kumar, Neeraj, Patil, Abhijeet, Kurian, Nikhil Cherian, Rane, Swapnil, Graham, Simon, Vu, Quoc Dang, Zwager, Mieke, Raza, Shan E. Ahmed, Rajpoot, Nasir, Wu, Xiyi, Chen, Huai, Huang, Yijie, Wang, Lisheng, Jung, Hyun, Brown, G. Thomas, Liu, Yanling, Liu, Shuolin, Jahromi, Seyed Alireza Fatemi, Khani, Ali Asghar, Montahaei, Ehsan, Baghshah, Mahdieh Soleymani, Behroozi, Hamid, Semkin, Pavel, Rassadin, Alexandr, Dutande, Prasad, Lodaya, Romil, Baid, Ujjwal, Baheti, Bhakti, Talbar, Sanjay, Mahbod, Amirreza, Ecker, Rupert, Ellinger, Isabella, Luo, Zhipeng, Dong, Bin, Xu, Zhengyu, Yao, Yuehan, Lv, Shuai, Feng, Ming, Xu, Kele, Zunair, Hasib, Hamza, Abdessamad Ben, Smiley, Steven, Yin, Tang-Kai, Fang, Qi-Rui, Srivastava, Shikhar, Mahapatra, Dwarikanath, Trnavska, Lubomira, Zhang, Hanyun, Narayanan, Priya Lakshmi, Law, Justin, Yuan, Yinyin, Tejomay, Abhiroop, Mitkari, Aditya, Koka, Dinesh, Ramachandra, Vikas, Kini, Lata, Sethi, Amit

“…Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer…”
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Artificial intelligence and machine learning applications for cultured meat by Todhunter, Michael E, Jubair, Sheikh, Verma, Ruchika, Saqe, Rikard, Shen, Kevin, Duffy, Breanna

“…Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological…”
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Novel MRI deformation-heterogeneity radiomic features are associated with molecular subgroups and overall survival in pediatric medulloblastoma: Preliminary findings from a multi-institutional study by Iyer, Sukanya, Ismail, Marwa, Tamrazi, Benita, Salloum, Ralph, de Blank, Peter, Margol, Ashley, Correa, Ramon, Chen, Jonathan, Bera, Kaustav, Statsevych, Volodymyr, Ho, Mai-Lan, Vaidya, Pranjal, Verma, Ruchika, Hawes, Debra, Judkins, Alexander, Fu, Pingfu, Madabhushi, Anant, Tiwari, Pallavi

“…Medulloblastoma (MB) is a malignant, heterogenous brain tumor. Advances in molecular profiling have led to identifying four molecular subgroups of MB (WNT,…”
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Convolutional neural networks for wavelet domain super resolution by Kumar, Neeraj, Verma, Ruchika, Sethi, Amit

“…•Proposed a super resolution method with higher reconstruction accuracy than before.•Cast super resolution as a problem of estimating sparse wavelet detail…”
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Some Properties of Range Operators on LCA Groups by VERMA, RUCHIKA, TEENA, KUMARI

“…In this paper, we study the structure of shift preserving operators acting on shift-invariant spaces in L2(G), where G is a locally compact Abelian group. We…”
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The Pursuit of Generalizability to Enable Clinical Translation of Radiomics by Tiwari, Pallavi, Verma, Ruchika

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Learning Individual Survival Models from PanCancer Whole Transcriptome Data by Kumar, Neeraj, Skubleny, Daniel, Parkes, Michael, Verma, Ruchika, Davis, Sacha, Kumar, Luke, Aissiou, Amira, Greiner, Russell

“…Personalized medicine attempts to predict survival time for each patient, based on their individual tumour molecular profile. We investigate whether our…”
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A Multi-Organ Nucleus Segmentation Challenge by Kumar, Neeraj, Verma, Ruchika, Anand, Deepak, Zhou, Yanning, Onder, Omer Fahri, Tsougenis, Efstratios, Chen, Hao, Heng, Pheng-Ann, Li, Jiahui, Hu, Zhiqiang, Wang, Yunzhi, Koohbanani, Navid Alemi, Jahanifar, Mostafa, Tajeddin, Neda Zamani, Gooya, Ali, Rajpoot, Nasir, Ren, Xuhua, Zhou, Sihang, Wang, Qian, Shen, Dinggang, Yang, Cheng-Kun, Weng, Chi-Hung, Yu, Wei-Hsiang, Yeh, Chao-Yuan, Yang, Shuang, Xu, Shuoyu, Yeung, Pak Hei, Sun, Peng, Mahbod, Amirreza, Schaefer, Gerald, Ellinger, Isabella, Ecker, Rupert, Smedby, Orjan, Wang, Chunliang, Chidester, Benjamin, Ton, That-Vinh, Tran, Minh-Triet, Ma, Jian, Do, Minh N., Graham, Simon, Vu, Quoc Dang, Kwak, Jin Tae, Gunda, Akshaykumar, Chunduri, Raviteja, Hu, Corey, Zhou, Xiaoyang, Lotfi, Dariush, Safdari, Reza, Kascenas, Antanas, O'Neil, Alison, Eschweiler, Dennis, Stegmaier, Johannes, Cui, Yanping, Yin, Baocai, Chen, Kailin, Tian, Xinmei, Gruening, Philipp, Barth, Erhardt, Arbel, Elad, Remer, Itay, Ben-Dor, Amir, Sirazitdinova, Ekaterina, Kohl, Matthias, Braunewell, Stefan, Li, Yuexiang, Xie, Xinpeng, Shen, Linlin, Ma, Jun, Baksi, Krishanu Das, Khan, Mohammad Azam, Choo, Jaegul, Colomer, Adrian, Naranjo, Valery, Pei, Linmin, Iftekharuddin, Khan M., Roy, Kaushiki, Bhattacharjee, Debotosh, Pedraza, Anibal, Bueno, Maria Gloria, Devanathan, Sabarinathan, Radhakrishnan, Saravanan, Koduganty, Praveen, Wu, Zihan, Cai, Guanyu, Liu, Xiaojie, Wang, Yuqin, Sethi, Amit

“…Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology…”
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NIMG-60. PREDICTING OVERALL SURVIVAL IN GLIOBLASTOMA USING HISTOPATHOLOGY VIA AN END-TO-END DEEP LEARNING PIPELINE: A LARGE MULTI-COHORT STUDY by Verma, Ruchika, Cohen, Mark, Toro, Paula, Mokhtari, Mojgan, Tiwari, Pallavi

“…Abstract PURPOSE Glioblastoma is an aggressive and universally fatal tumor. Morphological information as captured from cellular regions on surgically resected…”
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Author's Reply to "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge" by Verma, Ruchika, Kumar, Neeraj, Patil, Abhijeet, Kurian, Nikhil Cherian, Rane, Swapnil, Sethi, Amit

“…We had released MoNuSAC2020 as one of the largest publicly available, manually annotated, curated, multi-class, and multi-instance medical image segmentation…”
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Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors by Kumar, Neeraj, Verma, Ruchika, Chen, Chuheng, Lu, Cheng, Fu, Pingfu, Willis, Joseph, Madabhushi, Anant

“…We assessed the utility of quantitative features of colon cancer nuclei, extracted from digitized hematoxylin and eosin‐stained whole slide images (WSIs), to…”
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Technical Note: MRQy — An open‐source tool for quality control of MR imaging data by Sadri, Amir Reza, Janowczyk, Andrew, Zhou, Ren, Verma, Ruchika, Beig, Niha, Antunes, Jacob, Madabhushi, Anant, Tiwari, Pallavi, Viswanath, Satish E

“…Purpose There is an increasing availability of large imaging cohorts [such as through The Cancer Imaging Archive (TCIA)] for computational model development…”
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Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish s tage II and IV colon tumors by Kumar, Neeraj, Verma, Ruchika, Chen, Chuheng, Lu, Cheng, Fu, Pingfu, Willis, Joseph, Madabhushi, Anant

“…Abstract We assessed the utility of quantitative features of colon cancer nuclei, extracted from digitized hematoxylin and eosin‐stained whole slide images…”
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Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma by Beig, Niha, Bera, Kaustav, Prasanna, Prateek, Antunes, Jacob, Correa, Ramon, Singh, Salendra, Saeed Bamashmos, Anas, Ismail, Marwa, Braman, Nathaniel, Verma, Ruchika, Hill, Virginia B, Statsevych, Volodymyr, Ahluwalia, Manmeet S, Varadan, Vinay, Madabhushi, Anant, Tiwari, Pallavi

“…To (i) create a survival risk score using radiomic features from the tumor habitat on routine MRI to predict progression-free survival (PFS) in glioblastoma…”
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Abstract 3: Deep learning-based integration of esophageal cancer morphology with genomics by Verma, Ruchika, Wu, Wei, Kumar, Neeraj, Yu, Elizabeth, Choi, Won-Tak, Umetsu, Sarah, Bivona, Trever

“…Abstract Esophageal cancer is a major cause of cancer mortality world-wide. Even with advanced treatment options available, patients with esophageal cancer…”
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Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction by Qi, Shi-ang, Kumar, Neeraj, Verma, Ruchika, Xu, Jian-Yi, Shen-Tu, Grace, Greiner, Russell

“…An Individual Survival Distribution (ISD) models a patient's personalized survival probability at all future time points. Previously, ISD models have been…”
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NIMG-54. RADIOMIC FEATURES FROM LESION HABITAT PREDICT RESPONSE TO COMBINATION OF NIVOLUMAB AND BEVACIZUMAB IN PATIENTS WITH RECURRENT GLIOBLASTOMA: A FEASIBILITY STUDY by Verma, Ruchika, Rauf, Yasmeen, Yadav, Ipsa, Statsevych, Volodymyr, Chen, Jonathan, Evanoff, Wendi, Ahluwalia, Manmeet, Tiwari, Pallavi

“…Abstract PURPOSE The use of immunotherapy in glioblastoma management is under active investigation. Glioblastomas are “cold” tumors, meaning that they have…”
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TAMI-22. SEGMENTATION OF DISTINCT TUMOR HALLMARKS OF GLIOBLASTOMA ON DIGITAL HISTOPATHOLOGY USING A HIERARCHICAL DEEP LEARNING APPROACH by Sandino, Alvaro, Verma, Ruchika, Chen, Yijiang, Becerra, David, Romero, Eduardo, Tiwari, Pallavi

“…Abstract PURPOSE Glioblastoma is a highly heterogeneous brain tumor. Primary treatment for glioblastoma involves maximally-safe surgical resection. After…”
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Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset by Pati, Sarthak, Verma, Ruchika, Akbari, Hamed, Bilello, Michel, Hill, Virginia B., Sako, Chiharu, Correa, Ramon, Beig, Niha, Venet, Ludovic, Thakur, Siddhesh, Serai, Prashant, Ha, Sung Min, Blake, Geri D., Shinohara, Russell Taki, Tiwari, Pallavi, Bakas, Spyridon

“…Purpose The availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities…”
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