Fetching the paper…
Reading the bibliography…
Deep learning has been successfully applied to the segmentation of 3D Computed Tomography (CT) scans.
Liu, Y., Zhao, G., Nacewicz, B.M., Adluru, N., Kirk, G.R., Ferrazzano, P.A., Styner, M., Alexander, A.L.: Accurate automatic segmentation of amygdala subnuclei and modeling of uncertainty via bayesian fully convolutional neural network (2019), arXiv preprint 1902.07289
1902
Earlier work this paper cites.
Mitchell, T., Beauchamp, J.: Bayesian variable selection in linear regression. Journal of the American Statistical Association 83
1988
Earlier work this paper cites.
Hinton, G.E., Camp, D.V.: Keeping neural networks simple by minimizing the description length of the weights. In: Proceedings of the 16th Conference on Learning Theory (1993)
1993
Earlier work this paper cites.
Graves, A.: Practical variational inference for neural networks. In: Proceedings of the 24th Conference on Advances in Neural Information Processing Systems (2011)
2011
Earlier work this paper cites.
Damianou, A.C., Lawrence, N.D.: Deep gaussian processes. In: Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (2013)
2013
Earlier work this paper cites.
Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural networks. In: Proceedings of the 32nd International Conference on Machine Learning (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.L.: Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. In: Proceedings of the 28th Conference on Advances in Neural Information Processing Systems (2015)
2015
Earlier work this paper cites.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition (2015)
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (2015)
2015
Earlier work this paper cites.
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional neural networks. In: Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition (2015)
2015
Cited alongside, same era.
Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Józefowicz, R., Bengio, S.: Generating sentences from a continuous space. In: Proceedings of the SIGNLL Conference on Computational Natural Language Learning (2016), arXiv preprint 1511.06349
2016
Cited alongside, same era.
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d U-net: Learning dense volumetric segmentation from sparse annotation. In: Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention (2016)
2016
Cited alongside, same era.
Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on Machine Learning (2016)
Pietsch, P., Ebner, M., Marone, F., Stampanoni, M., Wood, V.: Determining the uncertainty in microstructural parameters extracted from tomographic data. Sustainable Energy Fuels 2
2018
Later among the works it cites.
Wen, Y., Vicol, P., Ba, J., Train, D., Grosse, R.: Flipout: Efficient pseudo-independent weight perturbations on mini-batches. In: Proceedings of the 6th International Conference on Learning Representations (2018)
2018
Later among the works it cites.
Wu, Y., He, K.: Group normalization. In: Proceedings of the 2018 European Conference on Computer Vision (2018)
2018
Later among the works it cites.
MacNeil, J.M.L., Ushizima, D.M., Panerai, F., Mansour, N.N., Barnard, H.S., Parkinson, D.Y.: Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means. Statistical Analysis and Data Mining: The ASA Data Science Journal 12
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of the 4th International Conference on 3D Vision (2016)
2016
Cited alongside, same era.
Osband, I.: Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout. In: Proceedings of the 29th Conference on Advances in Neural Information Processing Systems: Workshop on Bayesian Deep Learning (2016)
2016
Cited alongside, same era.
Dillon, J.V., Langmore, I., Tran, D., Brevdo, E., Vasudevan, S., Moore, D., Patton, B., Alemi, A., Hoffman, M., Saurous, R.A.: Tensorflow distributions (2017), arXiv preprint 1711.10604
2017
Cited alongside, same era.
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Proceedings of the 30th Conference on Advances in Neural Information Processing Systems (2017)
2017
Cited alongside, same era.
Konopczyński, T., Rathore, D., Rathore, J., Kröger, T., Zheng, L., Garbe, C.S., Carmignato, S., Hesser, J.: Fully convolutional deep network architectures for automatic short glass fiber semantic segmentation from ct scans. In: Proceedings of the 8th Conference on Industrial Computed Tomography (2018)
2018
Cited alongside, same era.
Müller, S., Pietsch, P., Brandt, B.E., Baade, P., De Andrade, V., De Carlo, F., Wood, V.: Quantification and modeling of mechanical degradation in lithium-ion batteries based on nanoscale imaging. Nature Communications 9
2018
Cited alongside, same era.
Norris, C., Mistry, A., Mukherjee, P.P., Roberts, S.A.: Microstructural screening for variability in graphite electrodes. In preparation
Cited in the paper.
Martinez, C., Potter, K.M., Smith, M.D., Donahue, E.A., Collins, L., Korbin, J.P., Roberts, S.A.: Segmentation certainty through uncertainty: Uncertainty-refined binary volumetric segmentation under multifactor domain shift. In: Proceedings of the 32nd IEEE Conference on Computer Vision and Pattern Recognition: Women in Computer Vision Workshop (2019)
2019
Closest in time.
Mukhoti, J., Gal, Y.: Evaluating bayesian deep learning methods for semantic segmentation (2019), arXiv preprint 1811.12709
2019
Closest in time.
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J.V., Lakshminarayanan, B., Snoek, J.: Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift. In: Proceedings of the 33rd Conference on Neural Information Processing Systems (2019)
2019
Closest in time.
Shridhar, K., Laumann, F., Liwicki, M.: Uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference (2019), arXiv preprint 1806.05978
2019
Closest in time.
Tran, D., Dusenberry, M.W., van der Wilk, M., Hafner, D.: Bayesian layers: A module for neural network uncertainty (2019), arXiv preprint 1812.03973
2019
Closest in time.