, Grand challenge on breast cancer histology images, 2018.
Prediction of survival after neoadjuvant chemotherapy for breast cancer by evaluation of tumor-infiltrating lymphocytes and residual cancer burden, BMC cancer, vol.17, issue.1, p.888, 2017. ,
Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer, Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, pp.666-669, 2010. ,
Random forests. Machine learning, vol.45, pp.5-32, 2001. ,
, , 2015.
Classification and disease localization in histopathology using only global labels: A weakly-supervised approach, 2018. ,
, Institut National du Cancer. Les cancers en France, 2017.
Weldon: Weakly supervised learning of deep convolutional neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4743-4752, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01343785
Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016. ,
Adam: A method for stochastic optimization, 2014. ,
Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012. ,
Convolutional neural networks for prostate cancer recurrence prediction, page 101400H. International Society for Optics and Photonics, vol.10140, 2017. ,
Ehteshami Bejnordi. 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset, GigaScience, vol.7, issue.6, p.65, 2018. ,
Segmentation of nuclei in histopathology images by deep regression of the distance map, IEEE Transactions on Medical Imaging, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01984033
Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy, Journal of Clinical Oncology, vol.25, issue.28, pp.4414-4422, 2007. ,
Predicting breast tumor proliferation from whole-slide images: the tupac16 challenge, 2018. ,
Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features, BMC bioinformatics, vol.18, issue.1, p.281, 2017. ,
Context-constrained multiple instance learning for histopathology image segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.623-630, 2012. ,
Wsisa: Making survival prediction from whole slide histopathological images, IEEE Conference on Computer Vision and Pattern Recognition, pp.7234-7242, 2017. ,