Convolutional Neural Networks for Medical Clustering
作者： Ashnil Kumar , Jinman Kim , Philip Heng Wai Leong , Dagan Feng , David Lyndon
DOI:
关键词:
摘要: A major challenge for Medical Image Retrieval (MIR) is the discovery of relationships between low-level image features (inten- sity, gradient, texture, etc.) and high-level semantics such as modal- ity, anatomy or pathology. Convolutional Neural Networks (CNNs) have been shown to an inherent ability automatically extract hier- archical representations from raw data. Their successful application in a variety generalised imaging tasks suggests great potential MIR. However, hurdle their deployment medical domain relative lack robust training corpora when compared general benchmarks ImageNET CIFAR. In this paper, we present adaptation CNNs clustering task at Image- CLEF 2015.
参考文章(17)
Yoshua Bengio, Xavier Glorot, Antoine Bordes, Antoine Bordes, Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach international conference on machine learning. pp. 513- 520 ,(2011)
Mauricio Villegas, Henning Müller, Andrew Gilbert, Luca Piras, Josiah Wang, Krystian Mikolajczyk, Alba G Seco De Herrera, Stefano Bromuri, M Ashraful Amin, Mahmood Kazi Mohammed, Burak Acar, Suzan Uskudarli, Neda B Marvasti, José F Aldana, María del Mar Roldán García, None, General Overview of ImageCLEF at the CLEF 2015 Labs cross language evaluation forum. ,vol. 9283, pp. 444- 461 ,(2015) , 10.1007/978-3-319-24027-5_45
Yoshua Bengio, Practical recommendations for gradient-based training of deep architectures Neural Networks: Tricks of the Trade (2nd ed.). pp. 437- 478 ,(2012) , 10.1007/978-3-642-35289-8_26
Ilya Sutskever, Geoffrey E. Hinton, Alex Krizhevsky, Ruslan R. Salakhutdinov, Nitish Srivastava, Improving neural networks by preventing co-adaptation of feature detectors arXiv: Neural and Evolutionary Computing. ,(2012)
Jayashree Kalpathy-Cramer, Alba García Seco de Herrera, Dina Demner-Fushman, Sameer Antani, Steven Bedrick, Henning Müller, Evaluating performance of biomedical image retrieval systems--an overview of the medical image retrieval task at ImageCLEF 2004-2013. Computerized Medical Imaging and Graphics. ,vol. 39, pp. 55- 61 ,(2015) , 10.1016/J.COMPMEDIMAG.2014.03.004
Jürgen Schmidhuber, Deep learning in neural networks Neural Networks. ,vol. 61, pp. 85- 117 ,(2015) , 10.1016/J.NEUNET.2014.09.003
Yaniv Bar, Idit Diamant, Lior Wolf, Hayit Greenspan, Deep learning with non-medical training used for chest pathology identification Proceedings of SPIE. ,vol. 9414, ,(2015) , 10.1117/12.2083124
Ilya Sutskever, Geoffrey Hinton, Alex Krizhevsky, Ruslan Salakhutdinov, Nitish Srivastava, Dropout: a simple way to prevent neural networks from overfitting Journal of Machine Learning Research. ,vol. 15, pp. 1929- 1958 ,(2014)
Ashnil Kumar, Jinman Kim, Weidong Cai, Michael Fulham, Dagan Feng, Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data. Journal of Digital Imaging. ,vol. 26, pp. 1025- 1039 ,(2013) , 10.1007/S10278-013-9619-2
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition Proceedings of the IEEE. ,vol. 86, pp. 2278- 2324 ,(1998) , 10.1109/5.726791