Srinivas C. Turaga
Srinivas C. Turaga
HHMI Janelia Research Campus
Verified email at - Homepage
Cited by
Cited by
Opportunities and obstacles for deep learning in biology and medicine
T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ...
Journal of The Royal Society Interface 15 (141), 20170387, 2018
Connectomic reconstruction of the inner plexiform layer in the mouse retina
M Helmstaedter, KL Briggman, SC Turaga, V Jain, HS Seung, W Denk
Nature 500 (7461), 168-174, 2013
Space–time wiring specificity supports direction selectivity in the retina
JS Kim, MJ Greene, A Zlateski, K Lee, M Richardson, SC Turaga, ...
Nature 509 (7500), 331-336, 2014
Convolutional networks can learn to generate affinity graphs for image segmentation
SC Turaga, JF Murray, V Jain, F Roth, M Helmstaedter, K Briggman, ...
Neural computation 22 (2), 511-538, 2010
In toto imaging and reconstruction of post-implantation mouse development at the single-cell level
K McDole, L Guignard, F Amat, A Berger, G Malandain, LA Royer, ...
Cell 175 (3), 859-876. e33, 2018
Crowdsourcing the creation of image segmentation algorithms for connectomics
I Arganda-Carreras, SC Turaga, DR Berger, D Cireşan, A Giusti, ...
Frontiers in neuroanatomy 9, 142, 2015
Mapping social behavior-induced brain activation at cellular resolution in the mouse
Y Kim, KU Venkataraju, K Pradhan, C Mende, J Taranda, SC Turaga, ...
Cell reports 10 (2), 292-305, 2015
Supervised learning of image restoration with convolutional networks
V Jain, JF Murray, F Roth, S Turaga, V Zhigulin, KL Briggman, ...
Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, 1-8, 2007
Large scale image segmentation with structured loss based deep learning for connectome reconstruction
J Funke, F Tschopp, W Grisaitis, A Sheridan, C Singh, S Saalfeld, ...
IEEE transactions on pattern analysis and machine intelligence 41 (7), 1669-1680, 2018
Machines that learn to segment images: a crucial technology for connectomics
V Jain, HS Seung, SC Turaga
Current opinion in neurobiology 20 (5), 653-666, 2010
Maximin affinity learning of image segmentation
SC Turaga, KL Briggman, M Helmstaedter, W Denk, HS Seung
NIPS, 1865-1873, 2009
Deep learning enables fast and dense single-molecule localization with high accuracy
A Speiser, LR Müller, P Hoess, U Matti, CJ Obara, WR Legant, A Kreshuk, ...
Nature methods 18 (9), 1082-1090, 2021
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data
P Berens, J Freeman, T Deneux, N Chenkov, T McColgan, A Speiser, ...
PLoS computational biology 14 (5), e1006157, 2018
Behavioral state coding by molecularly defined paraventricular hypothalamic cell type ensembles
S Xu, H Yang, V Menon, AL Lemire, L Wang, FE Henry, SC Turaga, ...
Science 370 (6514), eabb2494, 2020
Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set
J Buhmann, A Sheridan, C Malin-Mayor, P Schlegel, S Gerhard, ...
Nature Methods 18 (7), 771-774, 2021
Development of high-throughput, high-resolution 3D reconstruction of large-volume biological tissue using automated tape collection ultramicrotomy and scanning electron microscopy
R Schalek, N Kasthuri, K Hayworth, D Berger, J Tapia, J Morgan, ...
Microscopy and Microanalysis 17 (S2), 966-967, 2011
Method and apparatus for image processing
HS Seung, JF Murray, V Jain, SC Turaga, M Helmstaedter, W Denk
US Patent 9,799,098, 2017
Learning to Agglomerate Superpixel Hierarchies
V Jain, SC Turaga, KL Briggman, MN Helmstaedter, W Denk, HS Seung
Neural Information Processing Systems, 2011
Efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems
F Tschopp, JNP Martel, SC Turaga, M Cook, J Funke
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 1225-1228, 2016
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
A Speiser, J Yan, EW Archer, L Buesing, SC Turaga, JH Macke
Advances in neural information processing systems 30, 2017
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