Fetching the paper…
Reading the bibliography…
In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs).
Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y., 2013 · 1929
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., 2014 · 1958
Earlier work this paper cites.
Learning representations by back-propagating errors
Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1988 · 1988
Earlier work this paper cites.
Automatic tumor segmentation using knowledge-based clustering
Clark, M., Hall, L., Goldgof, D., Velthuizen, R.P., Murtagh, F., Silbiger, M.L., 1998 · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., 1998 · 1998
Earlier work this paper cites.
A generalized mean field algorithm for variational inference in exponential families, in: Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc.. pp. 583–591
Xing, E.P., Jordan, M.I., Russell, S., 2002 · 2002
Earlier work this paper cites.
Robust estimation for brain tumor segmentation, in: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2003. Springer, pp. 530–537
Prastawa, M., Bullitt, E., Ho, S., Gerig, G., 2003 · 2003
Earlier work this paper cites.
A brain tumor segmentation framework based on outlier detection
Prastawa, M., Bullit, E., Ho, S., Gerig, G., 2004 · 2004
Earlier work this paper cites.
Segmenting brain tumor with conditional random fields and support vector machines, in: in Proc of Workshop on Computer Vision for Biomedical Image Applications
Lee, C.H., Schmidt, M., Murtha, A., Bistritz, A., S, J., Greiner, R., 2005 · 2005
Earlier work this paper cites.
Segmenting brain tumors using alignment-based features, in: Int. Conf on Machine Learning and Applications, pp. 6–pp
Schmidt, M., Levner, I., Greiner, R., Murtha, A., Bistritz, A., 2005 · 2005
Earlier work this paper cites.
Glioma dynamics and computational models: A review of segmentation, registration, and in silico growth algorithms and their clinical applications 3
Angelini, E., Clatz, O., E., Konukoglu, E., Capelle, L., Duffau, H., 2007 · 2007
Earlier work this paper cites.
3d variational brain tumor segmentation using a high dimensional feature set, in: ICCV, pp. 1–8
Cobzas, D., Birkbeck, N., Schmidt, M., Jägersand, M., Murtha, A., 2007 · 2007
Earlier work this paper cites.
Advanced normalization tools (ants)
Avants, B.B., Tustison, N., Song, G., 2009 · 2009
Earlier work this paper cites.
What is the best multi-stage architecture for object recognition?, in: Computer Vision, 2009 IEEE 12th International Conference on, IEEE. pp. 2146–2153
Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y., 2009 · 2009
Earlier work this paper cites.
3d brain tumor segmentation in mri using fuzzy classification, symmetry analysis and spatially constrained deformable models
Khotanlou, H., Colliot, O., Atif, J., Bloch, I., 2009 · 2009
Earlier work this paper cites.
Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization., in: MICCAI, pp. 354–361
Bauer, S., Nolte, L.P., Reyes, M., 2011 · 2011
Earlier work this paper cites.
Domain adaptation for large-scale sentiment classification: A deep learning approach, in: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 513–520
Glorot, X., Bordes, A., Bengio, Y., 2011 · 2011
Cited alongside, same era.
Road scene segmentation from a single image, in: Proceedings of the 12th European Conference on Computer Vision - Volume Part VII, Springer-Verlag, Berlin, Heidelberg. pp. 376–389
Alvarez, J.M., Gevers, T., LeCun, Y., Lopez, A.M., 2012 · 2012
Cited alongside, same era.
Practical recommendations for gradient-based training of deep architectures, in: Neural Networks: Tricks of the Trade. Springer, pp. 437–478
Bengio, Y., 2012 · 2012
Cited alongside, same era.
Deep neural networks segment neuronal membranes in electron microscopy images, in: Advances in neural information processing systems, pp. 2843–2851
Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J., 2012 · 2012
Cited alongside, same era.
Brain tumor segmentation with deep neural networks
Davy, A., Havaei, M., Warde-Farley, D., Biard, A., Tran, L., Jodoin, P.M., Courville, A., Larochelle, H., Pal, C., Bengio, Y., 2014 · 2014
Later among the works it cites.
Brats 2014 Challenge Manuscripts
Farahani, K., Menze, B., Reyes, M., 2014 · 2014
Later among the works it cites.
Extremely randomized trees based brain tumor segmentation, in: in proc of BRATS Challenge - MICCAI
Gotz, M., Weber, C., Blocher, J., Stieltjes, B., Meinzer, H.P., Maier-Hein, K., 2014 · 2014
Later among the works it cites.
Simultaneous detection and segmentation, in: Computer Vision–ECCV 2014. Springer, pp. 297–312
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J., 2014 · 2014
Later among the works it cites.
Efficient interactive brain tumor segmentation as within-brain knn classification, in: International Conference on Pattern Recognition (ICPR)
Havaei, M., Jodoin, P.M., Larochelle, H., 2014 · 2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tumor-cut: Segmentation of brain tumors on contrast enhanced mr images for radiosurgery applications
Hamamci, A., Kucuk, N., Karaman, K., Engin, K., Unal, G., 2012 · 2012
Cited alongside, same era.
ImageNet classification with deep convolutional neural networks, in: NIPS
Krizhevsky, A., Sutskever, I., Hinton, G., 2012 · 2012
Cited alongside, same era.
Joint tumor segmentation and dense deformable registration of brain mr images., in: MICCAI, pp. 651–658
Parisot, S., Duffau, H., Chemouny, S., Paragios, N., 2012 · 2012
Cited alongside, same era.
3d variational brain tumor segmentation using dirichlet priors on a clustered feature set
Popuri, K., Cobzas, D., Murtha, A., Jägersand, M., 2012 · 2012
Cited alongside, same era.
Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel mr, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012. Springer, pp. 369–376
Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O., Das, T., Jena, R., Price, S., 2012 · 2012
Cited alongside, same era.
A survey of mri-based medical image analysis for brain tumor studies
Bauer, S., Wiest, R., Nolte, L., Reyes, M., 2013 · 2013
Cited alongside, same era.
Representation learning: A review and new perspectives
Bengio, Y., Courville, A., Vincent, P., 2013 · 2013
Cited alongside, same era.
Fully automatic brain tumor segmentation from multiple mr sequences using hidden markov fields and variational em
Doyle, S., Vasseur, F., Dojat, M., Forbes, F., 2013 · 2013
Cited alongside, same era.
ilastik for multi-modal brain tumor segmentation
Kleesiek, J., Biller, A., Urban, G., Kothe, U., Bendszus, M., Hamprecht, F.A., 2014 · 2014
Later among the works it cites.
Multimodal brain tumor image segmentation using glistr, in: in proc of BRATS Challenge - MICCAI
Kwon, D., Akbari, H., Da, X., Gaonkar, B., Davatzikos, C., 2014 · 2014
Later among the works it cites.
The multimodal brain tumor image segmentation benchmark (brats)
Menze, B., Reyes, M., Leemput, K.V., 2014 · 2014
Later among the works it cites.
Recurrent convolutional neural networks for scene labeling, in: Proceedings of The 31st International Conference on Machine Learning, pp. 82–90
Pinheiro, P., Collobert, R., 2014 · 2014
Later among the works it cites.
Appearance- and context-sensitive features for brain tumor segmentation, in: in proc of BRATS Challenge - MICCAI
R.Meier, S.Bauer, J.Slotboom, R.Wiest, M.Reyes, 2014 · 2014
Later among the works it cites.
Iterative multilevel mrf leveraging context and voxel information for brain tumour segmentation in mri
Subbanna, N., Precup, D., Arbel, T., 2014 · 2014
Later among the works it cites.
Multi-modal brain tumor segmentation using deep convolutional neural networks
Urban, G., Bendszus, M., Hamprecht, F., Kleesiek, J., 2014 · 2014
Later among the works it cites.
Visualizing and understanding convolutional networks, in: Computer Vision–ECCV 2014. Springer, pp. 818–833
Zeiler, M.D., Fergus, R., 2014 · 2014
Later among the works it cites.
Segmentation of brain tumor tissues with convolutional neural networks
Zikic, D., Ioannou, Y., Brown, M., Criminisi, A., 2014 · 2014
Later among the works it cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T., 2015 · 2015
Closest in time.