Multiscale convolutional neural networks for vision-based classification of cells

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7725 LNCS, Issue PART 2, 2013, Pages 342-352

  Buyssens, Pierre ^a   Elmoataz, Abderrahim ^a   Lézoray, Olivier ^a  

^a UNIVERSITÉ DE CAEN   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION RATES; CONVOLUTIONAL NEURAL NETWORK; DIFFERENT RESOLUTIONS; FEATURES EXTRACTION; MULTISCALES; PROPOSED ARCHITECTURES; STATE-OF-THE-ART METHODS; VISION BASED;

COMPUTER AIDED DIAGNOSIS;

NEURAL NETWORKS;

EID: 84875908988     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-37444-9_27     Document Type: Conference Paper

References (14)

1. Malek, J., Sebri, A., Mabrouk, S., Torki, K., Tourki, R.: Automated breast cancer diagnosis based on gvf-snake segmentation, wavelet features extraction and fuzzy classification. J. Signal Process. Syst. 55, 49-66 (2009)
2. Puls, J.H., Dytch, H.E., Roman, M.R.: Automated screening for cervical cancer using image processing techniques. In: Proceedings of 1980 FPS Users Group Meeting (1980)
3. Churg, A., Cagle, P.T., Roggli, V.L.: Tumors of the Serosal Membrane. AFIP Atlas of Tumor Pathology - Series 4. American Registry of Pathology (2006)
4. Zarzycki, M., Schneider, T.E., Meyer-Ebrecht, D., Böcking, A.: Classification of cell types in feulgen stained cytologic specimens using morphologic features. In: Bildverarbeitung für die Medizin, 410-414 (2005)
5. Nebauer, C.: Evaluation of convolutional neural networks for visual recognition. IEEE Transactions on Neural Networks 9, 685-696 (1998)
6. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of IEEE 86, 2278-2324 (1998)
7. Hubel, D.H., Wiesel, T.: Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Physiol. 160, 106-154 (1962)
8. Scherer, D., Müller, A., Behnke, S.: Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part III. LNCS, vol. 6354, pp. 92-101. Springer, Heidelberg (2010)
9. Meier, U., Ciresan, D.C., Gambardella, L.M., Schmidhuber, J.: Better digit recognition with a committee of simple neural nets. In: ICDAR, pp. 1250-1254. IEEE (2011)
10. Ueda, N.: Optimal linear combination of neural networks for improving classification performance. PAMI 22, 207-215 (2000)
11. Rodenacker, K.: A feature set for cytometry on digitized microscopic images. Cell. Pathol. 25, 1-36 (2001)
12. Wolf, G., Beil, M., Guski, H.: Chromatin structure analysis based on a hierarchic texture model. Analytical and Quantitative Cytology and Histology 17, 25-34 (1995)
13. Julesz, B.: Textons, the elements of texture perception, and their interactions. Nature 290, 91-97 (1981)
14. Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y.: SimpleMKL (2008)