Comparison of Deep Learning Models for Cancer Metastases Detection: An Experimental Study
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[1] Nasir M. Rajpoot,et al. Significance of Hyperparameter Optimization for Metastasis Detection in Breast Histology Images , 2018, COMPAY/OMIA@MICCAI.
[2] Xin Wang,et al. Invasive Cancer Detection Utilizing Compressed Convolutional Neural Network and Transfer Learning , 2018, MICCAI.
[3] Max Welling,et al. Rotation Equivariant CNNs for Digital Pathology , 2018, MICCAI.
[4] Yi Li,et al. Cancer Metastasis Detection With Neural Conditional Random Field , 2018, ArXiv.
[5] Zhang Yi,et al. Breast cancer cell nuclei classification in histopathology images using deep neural networks , 2018, International Journal of Computer Assisted Radiology and Surgery.
[6] Andrew H. Beck,et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.
[7] Joel H. Saltz,et al. Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images , 2017, Pattern Recognit..
[8] Aleksey Boyko,et al. Detecting Cancer Metastases on Gigapixel Pathology Images , 2017, ArXiv.
[9] Jianzhong Wu,et al. Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images , 2016, IEEE Transactions on Medical Imaging.
[10] Andrew Janowczyk,et al. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases , 2016, Journal of pathology informatics.
[11] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[12] Dimitrios I. Fotiadis,et al. Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.
[13] Jianzhong Wu,et al. Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).
[14] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[15] M. Kramer. Nonlinear principal component analysis using autoassociative neural networks , 1991 .
[16] A. Jemal,et al. Breast Cancer Statistics , 2013 .