GECNN-CRF for Prostate Cancer Detection with WSI

Pathological examination is of great significance for the diagnosis and treatment of prostate cancer, but artificial pathological examination is time-consuming, laborious and error prone. Automatic pathological examination can assist doctors in diagnosis and treatment. In this paper, a deep learning convolution network based on group equivariant convolution and conditional random field is proposed, which solves the problem of inconsistent features of data after random rotation in the learning process and enhances the robustness of the network. On the other hand, the conditional random field method is used to produce the same result on the slice image, which makes the segmentation edge of the probability image clearer.

[1]  Dit-Yan Yeung,et al.  Learning a Deep Compact Image Representation for Visual Tracking , 2013, NIPS.

[2]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[3]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Dayong Wang,et al.  Deep Learning for Identifying Metastatic Breast Cancer , 2016, ArXiv.

[5]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Aleksey Boyko,et al.  Detecting Cancer Metastases on Gigapixel Pathology Images , 2017, ArXiv.

[7]  Yi Li,et al.  Cancer Metastasis Detection With Neural Conditional Random Field , 2018, ArXiv.

[8]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Bai Ying Lei,et al.  Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning , 2015, IEEE Transactions on Biomedical Engineering.

[10]  Linda G. Shapiro,et al.  ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation , 2018, ECCV.

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Max Welling,et al.  Group Equivariant Convolutional Networks , 2016, ICML.

[13]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[14]  Angel Cruz-Roa,et al.  Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features , 2014, Journal of medical imaging.

[15]  Max Welling,et al.  Rotation Equivariant CNNs for Digital Pathology , 2018, MICCAI.

[16]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[17]  Joel H. Saltz,et al.  Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[20]  Luca Bertinetto,et al.  Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.

[21]  Catarina Eloy,et al.  Classification of breast cancer histology images using Convolutional Neural Networks , 2017, PloS one.

[22]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.