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

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MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge von 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

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Artificial intelligence and machine learning applications for cultured meat von Todhunter, Michael E, Jubair, Sheikh, Verma, Ruchika, Saqe, Rikard, Shen, Kevin, Duffy, Breanna

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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-i... von 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

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Convolutional neural networks for wavelet domain super resolution von Kumar, Neeraj, Verma, Ruchika, Sethi, Amit

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Some Properties of Range Operators on LCA Groups von VERMA, RUCHIKA, TEENA, KUMARI

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The Pursuit of Generalizability to Enable Clinical Translation of Radiomics von Tiwari, Pallavi, Verma, Ruchika

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

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A Multi-Organ Nucleus Segmentation Challenge von 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

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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 von Verma, Ruchika, Cohen, Mark, Toro, Paula, Mokhtari, Mojgan, Tiwari, Pallavi

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Author's Reply to "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge" von Verma, Ruchika, Kumar, Neeraj, Patil, Abhijeet, Kurian, Nikhil Cherian, Rane, Swapnil, Sethi, Amit

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Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors von Kumar, Neeraj, Verma, Ruchika, Chen, Chuheng, Lu, Cheng, Fu, Pingfu, Willis, Joseph, Madabhushi, Anant

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Technical Note: MRQy — An open‐source tool for quality control of MR imaging data von Sadri, Amir Reza, Janowczyk, Andrew, Zhou, Ren, Verma, Ruchika, Beig, Niha, Antunes, Jacob, Madabhushi, Anant, Tiwari, Pallavi, Viswanath, Satish E

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Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish s tage II and IV colon tumors von Kumar, Neeraj, Verma, Ruchika, Chen, Chuheng, Lu, Cheng, Fu, Pingfu, Willis, Joseph, Madabhushi, Anant

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Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma von 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

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Abstract 3: Deep learning-based integration of esophageal cancer morphology with genomics von Verma, Ruchika, Wu, Wei, Kumar, Neeraj, Yu, Elizabeth, Choi, Won-Tak, Umetsu, Sarah, Bivona, Trever

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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 von Verma, Ruchika, Rauf, Yasmeen, Yadav, Ipsa, Statsevych, Volodymyr, Chen, Jonathan, Evanoff, Wendi, Ahluwalia, Manmeet, Tiwari, Pallavi

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Clinico-epidemiological Profile of ENT Cases at Tertiary Care Center of Western UP: An Institutional Retrospective Study von Gautam, Harendra, Zehra, Azme, Verma, Ruchika

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Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset von 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

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NIMG-26. RADIOMIC FEATURES OF GLIOBLASTOMA ON PRE-TREATMENT GD-T1W MRI ARE PREDICTIVE OF RESPONSE TO CHEMO-RADIATION THERAPY AND ASSOCIATED WITH AKT AND APOPTOSIS PATHWAYS von Beig, Niha, Prasanna, Prateek, Verma, Ruchika, Hill, Virginia, Varadan, Vinay, Madabhushi, Anant, Tiwari, Pallavi

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