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A. Singanamalli · SciRah

Researcher profile · Semantic Scholar

A. Singanamalli

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A. Singanamalli — researcher profile on SciRah

h-index: 7
Works: 14
Citations: 248

Most-cited papers

Supervised Multi-View Canonical Correlation Analysis (sMVCCA): Integrating Histologic and Proteomic Features for Predicting Recurrent Prostate Cancer
George Lee, A. Singanamalli, Hai-Bo Wang, M. Feldman +6 more
2015IEEE Transactions on Medical Imaging85 citations
Semantic Scholar
Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings
Gregory Penzias, A. Singanamalli, R. Elliott, J. Gollamudi +12 more
2018PLoS ONE59 citations Open access
Semantic Scholar
Identifying in vivo DCE MRI markers associated with Microvessel Architecture and Gleason Grades of Prostate Cancer
A. Singanamalli, M. Rusu, R. Sparks, N. Shih +6 more
2015Journal of Magnetic Resonance Imaging37 citations Open access
Semantic Scholar
Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features
A. Singanamalli, Hai-Bo Wang, A. Madabhushi, M. Weiner +237 more
2017Scientific Reports23 citations Open access
Semantic Scholar
AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments
Gregory Penzias, A. Janowczyk, A. Singanamalli, M. Rusu +10 more
2016Scientific Reports14 citations Open access
Semantic Scholar
Supervised multi-view canonical correlation analysis: fused multimodal prediction of disease diagnosis and prognosis
A. Singanamalli, Hai-Bo Wang, George Lee, N. Shih +5 more
2014Medical Imaging12 citations
Semantic Scholar
Identifying in vivo DCE MRI parameters correlated with ex vivo quantitative microvessel architecture: A radiohistomorphometric approach
A. Singanamalli, R. Sparks, M. Rusu, N. Shih +5 more
2013Medical Imaging7 citations
Semantic Scholar
Improved automated segmentation of human kidney organoids using deep convolutional neural networks
Michael MacDonald, T. R. Fennel, A. Singanamalli, N. Cruz +3 more
2020Image Processing6 citations
Semantic Scholar
Improved noninvasive prostate cancer assessment using multiparametric magnetic resonance imaging
Xia Li, A. Singanamalli, D. Shanbhag, A. Hötker +3 more
2016IEEE International Symposium on Biomedical Imaging2 citations
Semantic Scholar
Selecting Features with Group-Sparse Nonnegative Supervised Canonical Correlation Analysis: Multimodal Prostate Cancer Prognosis
Hai-Bo Wang, A. Singanamalli, S. B. Ginsburg, A. Madabhushi
2014International Conference on Medical Image Computing and Computer-Assisted Intervention2 citations
Semantic Scholar
Blood flow anomaly detection via generative adversarial networks: a preliminary study
A. Singanamalli, J. Mitra, Kirk Wallace, P. Venugopal +6 more
2020Image-Guided Procedures1 citations
Semantic Scholar
Abstract P2-03-01: Computer extracted image texture features on T2-weighted MRI appear to correlate with nuclear morphologic descriptors from H&E-stained histopathology in estrogen receptor positive breast cancers
A. Madabhushi, A. Basavanhally, Scott Doyle, T. Wan +5 more
20130 citations
Semantic Scholar
Radiomics Driven Image Analysis and Coregistration Scheme to Identify DCE MRI Markers for Microvascular Density
A. Singanamalli, R. Sparks, M. Rusu, N. Shih +5 more
20120 citations
Semantic Scholar
Finite element anaylsis of the anatomy of acupuncture points
D. Shreiber, A. Singanamalli, Margaret Julias, H. Buettner
20090 citations
Semantic Scholar

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