Christoph-Nikolas Straehle
Christoph-Nikolas Straehle
Research Scientist, Bosch Center for Artificial Intelligence
Verified email at
Cited by
Cited by
Ilastik: interactive machine learning for (bio) image analysis
S Berg, D Kutra, T Kroeger, CN Straehle, BX Kausler, C Haubold, ...
Nature methods 16 (12), 1226-1232, 2019
Ilastik: Interactive learning and segmentation toolkit
C Sommer, C Straehle, U Koethe, FA Hamprecht
2011 IEEE international symposium on biomedical imaging: From nano to macro …, 2011
Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images
A Kreshuk, CN Straehle, C Sommer, U Koethe, M Cantoni, G Knott, ...
PloS one 6 (10), e24899, 2011
Globally consistent multi-label assignment on the ray space of 4d light fields
S Wanner, C Straehle, B Goldluecke
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013
Conditional flow variational autoencoders for structured sequence prediction
A Bhattacharyya, M Hanselmann, M Fritz, B Schiele, CN Straehle
arXiv preprint arXiv:1908.09008, 2019
Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons
B Maco, A Holtmaat, M Cantoni, A Kreshuk, CN Straehle, FA Hamprecht, ...
PloS one 8 (2), e57405, 2013
Carving: scalable interactive segmentation of neural volume electron microscopy images
CN Straehle, U Köthe, G Knott, FA Hamprecht
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011: 14th …, 2011
Biomedical imaging: from Nano to Macro
C Sommer, C Straehle, U Kothe, FA Hamprecht
2011 IEEE International Symposium on 230, 233, 2011
Automated segmentation of synapses in 3D EM data
A Kreshuk, CN Straehle, C Sommer, U Koethe, G Knott, FA Hamprecht
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro …, 2011
Seeded watershed cut uncertainty estimators for guided interactive segmentation
CN Straehle, U Koethe, G Knott, K Briggman, W Denk, FA Hamprecht
2012 IEEE Conference on Computer Vision and Pattern Recognition, 765-772, 2012
Learning game-theoretic models of multiagent trajectories using implicit layers
P Geiger, CN Straehle
Proceedings of the AAAI Conference on Artificial Intelligence 35 (6), 4950-4958, 2021
Adenosine and forskolin inhibit platelet aggregation by collagen but not the proximal signalling events
JC Clark, DM Kavanagh, S Watson, JA Pike, RK Andrews, EE Gardiner, ...
Thrombosis and Haemostasis 119 (07), 1124-1137, 2019
Haar wavelet based block autoregressive flows for trajectories
A Bhattacharyya, CN Straehle, M Fritz, B Schiele
Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen …, 2021
Including multi-feature interactions and redundancy for feature ranking in mixed datasets
AK Shekar, T Bocklisch, PI Sánchez, CN Straehle, E Müller
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017
Multiple instance learning with response-optimized random forests
C Straehle, M Kandemir, U Koethe, FA Hamprecht
2014 22nd International Conference on Pattern Recognition, 3768-3773, 2014
K-smallest spanning tree segmentations
C Straehle, S Peter, U Köthe, FA Hamprecht
Pattern Recognition: 35th German Conference, GCPR 2013, Saarbrücken, Germany …, 2013
Weakly supervised learning of image partitioning using decision trees with structured split criteria
C Straehle, U Koethe, FA Hamprecht
Proceedings of the IEEE International Conference on Computer Vision, 1849-1856, 2013
Machine learnable system with conditional normalizing flow
A Bhattacharyya, CN Straehle
US Patent App. 16/919,955, 2021
Imitation learning by state-only distribution matching
D Boborzi, CN Straehle, JS Buchner, L Mikelsons
Applied Intelligence 53 (24), 30865-30886, 2023
Non-cooperative multi-agent systems with exploring agents
J Etesami, CN Straehle
arXiv preprint arXiv:2005.12360, 2020
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