Bach: Grand challenge on breast cancer histology images, 2018. ,
Segnet: A deep convolutional encoder-decoder architecture for image segmentation, 2017. ,
Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Science translational medicine, 2011. ,
Dcan: Deep contour-aware networks for accurate gland segmentation, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp.2487-2496, 2016. ,
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, 2016. ,
Accurate and reproducible invasive breast cancer detection in wholeslide images: A deep learning approach for quantifying tumor extent ,
Patch-based convolutional neural network for whole slide tissue image classification, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. ,
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases, Journal of Pathology Informatics, 2016. ,
Adam: A method for stochastic optimization, 2014. ,
, Ground truth U-Net SegNet FCN DeepLab Invasive carsinoma Carsinoma In situ Begnin epithelum Simple stroma Complex stroma Adipose tissue Background Figure 5: From left to right, two test ground truth masks, U-Net, SegNet, FCN and DeepLab multi-classes segmentation results using optimal parameters
Deep learning. nature ,
Fully convolutional networks for semantic segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.3431-3440, 2015. ,
Breast cancer characterization based on image classification of tissue sections visualized under low magnification. Computational and mathematical methods in medicine, 2013. ,
A method for normalizing histology slides for quantitative analysis, Biomedical Imaging: From Nano to Macro, 2009. ISBI'09. IEEE International Symposium on ,
U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention ,
Empirical comparison of color normalization methods for epithelial-stromal classification in h and e images, Journal of pathology informatics ,
Best practices for convolutional neural networks applied to visual document analysis, 2003. ,
A dataset for breast cancer histopathological image classification, IEEE Transactions on Biomedical Engineering ,
URL : https://hal.archives-ouvertes.fr/hal-02113843
Structure-preserving color normalization and sparse stain separation for histological images, IEEE transactions on medical imaging, 2016. ,
, Deep learning for identifying metastatic breast cancer, 2016.