IODA: An input/output deep architecture for image labeling

Pattern Recognition

Volumn 48, Issue 9, 2015, Pages 2847-2858

  Lerouge, J ^a   Herault, R ^a   Chatelain, C ^a   Jardin, F ^c   Modzelewski, R ^a,b  

^a NORMANDIE UNIVERSITÉ   (France)

^b ROUEN UNIVERSITY HOSPITAL   (France)

^c INSERM   (France)

Author keywords

Deep learning architectures; Deep neural network; Image labeling; Machine learning; Medical imaging; Sarcopenia

Indexed keywords

DEEP LEARNING; DEEP NEURAL NETWORKS; IMAGE CODING; LEARNING SYSTEMS; MEDICAL IMAGING; NEURAL NETWORKS; TEXTURES;

DEEP ARCHITECTURES; IMAGE LABELING; LEARNING ARCHITECTURES; MEDICAL DOMAINS; NETWORK FRAMEWORKS; OPEN SOURCE IMPLEMENTATION; SARCOPENIA; TEXTURE PROBLEMS;

NETWORK ARCHITECTURE;

EID: 84929516076     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2015.03.017     Document Type: Article

References (40)

1. H. Wang, J. Suh, S. Das, J. Pluta, C. Craige, and P. Yushkevich Multi-atlas segmentation with joint label fusion IEEE Trans. Pattern Anal. Mach. Intell. 35 3 2013 611 623
2. B. Zitova, and J. Flusser Image registration methods a survey Image Vis. Comput. 21 11 2003 977 1000
3. J.B.A. Maintz, and M.A. Viergever A survey of medical image registration Med. Image Anal. 2 1 1998 1 36
4. A. Goshtasby Piecewise linear mapping functions for image registration Pattern Recognit. 19 6 1986 459 466
5. H. Chung, D. Cobzas, L. Birdsell, J. Lieffers, V. Baracos, Automated segmentation of muscle and adipose tissue on ct images for human body composition analysis, Proc. SPIE 7261 (2009) 72610K-72610K-8.
6. O. Chum, A. Zisserman, An exemplar model for learning object classes, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1-8.
7. V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid Groups of adjacent contour segments for object detection IEEE Trans. Pattern Anal. Mach. Intell. 30 1 2008 36 51
8. G. Csurka, and F. Perronnin An efficient approach to semantic segmentation Int. J. Comput. Vis. 95 2 2011 198 212
9. P. Kohli, L. Ladický, and P.H. Torr Robust higher order potentials for enforcing label consistency Int. J. Comput. Vis. 82 3 2009 302 324
10. L. Rabiner A tutorial on hidden Markov models and selected applications in speech recognition Proceedings of the IEEE 77 2 1989 257 286
11. J. Lafferty, A. McCallum, F. Pereira, Conditional random fields: probabilistic models for segmenting and labeling sequence data, in: ICML, 2001, pp. 282-289.
12. S. Nicolas, T. Paquet, and L. Heutte A Markovian approach for handwritten document segmentation Int. Conf. Pattern Recognit. 3 2006 292 295
13. S.Z. Li, Markov Random Field Modeling in Image Analysis, 3rd edition, Springer Publishing Company, Incorporated, 2009, http://dx.doi.org/10.1007/978-1-84800-279-1.
14. A. Ion, J. Carreira, and C. Sminchisescu Probabilistic joint image segmentation and labeling by figure-ground composition Int. J. Comput. Vis. 107 1 2014 40 57
15. P. Krähenbühl, V. Koltun, Efficient inference in fully connected CRFs with Gaussian edge potentials, in: Neural Information Processing Systems, 2011, pp. 109-117.
16. J. Besag On the statistical analysis of dirty pictures J. R. Soc. Ser. B 48 1986 259 302
17. P.B. Chou, C.M. Brown, The theory and practice of Bayesian image labeling, Int. J. Comput. Vis. 4 (1990) 185-210.
18. M.B. Blaschko, C.H. Lampert, Learning to localize objects with structured output regression, in: ECCV, 2008, pp. 2-15.
19. J. Weston, O. Chapelle, A. Elisseeff, B. Scholkopf, V. Vapnik, Kernel dependency estimation, in: Neural Information Processing System, 2002, pp. 873-880.
20. G.E. Hinton, S. Osindero, and Y.-W. Teh A fast learning algorithm for deep belief nets Neural Comput. 18 7 2006 1527 1554
21. Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, Greedy layer-wise training of deep networks, in: Neural Information Processing System, 2007, pp. 153-160.
22. J. Weston, F. Ratle, R. Collobert, Deep learning via semi-supervised embedding, in: ICML, 2008, pp. 1168-1175.
23. N. Srivastava, Improving Neural Networks with Dropout (Masters thesis), University of Toronto, Toronto, Canada, January 2013.
24. R. Sarikaya, G.E. Hinton, and A. Deoras Application of deep belief networks for natural language understanding IEEE/ACM Trans. Audio, Speech Lang. Process. 22 4 2014 778 784
25. L. Deng, G.E. Hinton, B. Kingsbury, New types of deep neural network learning for speech recognition and related applications: an overview, in: ICASSP, 2013, pp. 8599-8603.
26. A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber A novel connectionist system for unconstrained handwriting recognition IEEE Trans. Pattern Anal. Mach. Intell. 31 5 2009 855 868
27. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Parallel distributed processing: explorations in the microstructure of cognition, in: Learning Internal Representations by Error Propagation, vol. 1, MIT Press, Cambridge, MA, USA, 1986, pp. 318-362.
28. D.C. Ciresan, U. Meier, L.M. Gambardella, and J. Schmidhuber Deep big simple neural nets excel on handwritten digit recognition Neural Comput. 22 12 2010 3207 3220
29. B. Labbe, R. Herault, C. Chatelain, Learning deep neural networks for high dimensional output problems, in: ICMLA, Miami, USA, 2009, p. 6.
30. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol Stacked denoising autoencoders learning useful representations in a deep network with a local denoising criterion J. Mach. Learn. Res. 11 2010 3371 3408
31. Y. Bengio, A. Courville, and P. Vincent Representation learning a review and new perspectives IEEE Trans. Pattern Anal. Mach. Intell. 35 8 2013 1798 1828
32. H. Bourlard, and Y. Kamp Auto-association by multilayer perceptrons and singular value decomposition Biol. Cybern. 59 4-5 1988 291 294
33. J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, Y. Bengio. Theano: A CPU and GPU Math Expression Compiler. Proceedings of the Python for Scientific Computing Conference (SciPy) 2010. June 30 - July 3, Austin, TX.
34. X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in: In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS10), Society for Artificial Intelligence and Statistics, 2010.
35. L. Martin, L. Birdsell, N. MacDonald, T. Reiman, M.T. Clandinin, L.J. McCargar, R. Murphy, S. Ghosh, M.B. Sawyer, and V.E. Baracos Cancer cachexia in the age of obesity skeletal muscle depletion is a powerful prognostic factor, Independent of body mass index J. Clin. Oncol. 31 12 2013 1539 1547
36. H. Lanic, J. Kraut-Tauzia, R. Modzelewski, F. Clatot, S. Mareschal, J.-M. Picquenot, A. Stamatoullas, S. Leprêtre, H. Tilly, and F. Jardin Sarcopenia is an independent prognostic factor in elderly patients with diffuse large B-cell lymphoma treated with immunochemotherapy Leuk. Lymphoma 55 4 2014 817 823 10.3109/10428194.2013.816421. Epub 2013 Jul 29
37. V. Papavassiliou, T. Stafylakis, V. Katsouros, and G. Carayannis Handwritten document image segmentation into text lines and words Pattern Recognit. 43 1 2010 369 377
38. P. Barlas, S. Adam, C. Chatelain, T. Paquet, A typed and handwritten text block segmentation system for heterogeneous and complex documents, in: Document Analysis Systems, 2014, pp. 46-50.
39. S.M. Bascón, J.A. Rodríguez, S.L. Arroyo, A.F. Caballero, and F. López-Ferreras An optimization on pictogram identification for the road-sign recognition task using SVMs Comput. Vis. Image Understand. 114 3 2010 373 383
40. A. Ruta, Y. Li, and X. Liu Real-time traffic sign recognition from video by class-specific discriminative features Pattern Recognit. 43 1 2010 416 430