Tumor Cell Identification in Ki-67 Images on Deep Learning

The proportion of cells staining for the nuclear antigen Ki-67 is an important predictive indicator for assessment of tumor cell proliferation and growth in routine pathological investigation. Instead of traditional scoring methods based on the experience of a trained laboratory scientist, deep learning approach can be automatically used to analyze the expression of Ki-67 as well. Deep learning based on convolutional neural networks (CNN) for image classification and single shot multibox detector (SSD) for object detection are used to investigate the expression of Ki-67 for assessment of biopsies from patients with breast cancer in this study. The results focus on estimating the probability heatmap of tumor cells using CNN with accuracy of 98% and detecting the tumor cells using SSD with accuracy of 90%. This deep learning framework will provide an objective basis for the malignant degree of breast tumors and be beneficial to the pathologists for fast and efficiently Ki-67 scoring.

[1]  Fuchun Sun,et al.  RON: Reverse Connection with Objectness Prior Networks for Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[3]  Johannes Gerdes,et al.  The Ki‐67 protein: From the known and the unknown , 2000, Journal of cellular physiology.

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

[5]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[6]  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.

[7]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[8]  Ying Liu,et al.  Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks , 2018, ArXiv.

[9]  C. Chakraborty,et al.  An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer , 2017, Scientific Reports.

[10]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

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

[12]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[13]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[15]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[17]  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.

[18]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

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

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

[21]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  H Stein,et al.  Cell cycle analysis of a cell proliferation-associated human nuclear antigen defined by the monoclonal antibody Ki-67. , 1984, Journal of immunology.

[23]  Shuicheng Yan,et al.  A survey on deep learning-based fine-grained object classification and semantic segmentation , 2017, International Journal of Automation and Computing.

[24]  Junnian Zheng,et al.  Ki67 is a promising molecular target in the diagnosis of cancer (review). , 2015, Molecular medicine reports.

[25]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.