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Asha Singanamalli
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2020 – today
- 2020
- [c5]Asha Singanamalli, Jhimli Mitra, Kirk Wallace, Prem Venugopal, Scott Smith, Larry Mo, Lai Yee Leung, Jonathan Morrison, Todd Rasmussen, Luca Marinelli:
Blood flow anomaly detection via generative adversarial networks: a preliminary study. Image-Guided Procedures 2020: 1131522 - [c4]Michael MacDonald, Theron R. Fennel, Asha Singanamalli, Nelly M. Cruz, Mohammad Yousefhussein, Yousef Al-Kofahi, Benjamin S. Freedman:
Improved automated segmentation of human kidney organoids using deep convolutional neural networks. Image Processing 2020: 113133B
2010 – 2019
- 2016
- [c3]Xia Li, Asha Singanamalli, Dattesh Shanbhag, Andreas M. Hötker, Omer Aras
, Oguz Akin, Roshni Bhagalia:
Improved noninvasive prostate cancer assessment using multiparametric magnetic resonance imaging. ISBI 2016: 1199-1203 - 2015
- [j1]George Lee, Asha Singanamalli, Haibo Wang, Michael D. Feldman, Stephen R. Master
Supervised Multi-View Canonical Correlation Analysis (sMVCCA): Integrating Histologic and Proteomic Features for Predicting Recurrent Prostate Cancer. IEEE Trans. Medical Imaging 34(1): 284-297 (2015) - 2014
- [c2]Haibo Wang, Asha Singanamalli, Shoshana Ginsburg, Anant Madabhushi
Selecting Features with Group-Sparse Nonnegative Supervised Canonical Correlation Analysis: Multimodal Prostate Cancer Prognosis. MICCAI (3) 2014: 385-392 - 2013
- [c1]Asha Singanamalli, Rachel Sparks
Identifying in vivo DCE MRI parameters correlated with ex vivo quantitative microvessel architecture: A radiohistomorphometric approach. Digital Pathology 2013: 867604
Coauthor Index
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