中图分类号：TP18         文献标识码：A         文章编号：2096-4706（2018）05-0170-03

Application of Artificial Intelligence for Precision Medicine in Pathological Image
WANG Yipei¹，YAN Wen¹，ZHANG Yizhao²，LAI Maode³，XU Yan^1，2
（1.School of Biological and Medical Engineering，Beihang University，Beijing 100191，China；2.Microsoft Research Asia，Beijing  100080，China；3.Department of Pathology，School of Medicine，Zhejiang University，Hangzhou 310058，China）

Abstract：Precision medicine aims to integrate a variety of medical big data to tailor the patient's best treatment plan in order to achieve maximum treatment effects and minimize side effects. Artificial intelligence algorithm has remarkable advantages in computer vision field because of its powerful feature extraction ability. Applying artificial intelligence to medical scenarios，especially pathological image analysis，has greatly promoted the development of computer-aided diagnosis and provided potential and vitality for precision medicine.

Keywords：pathological section images；precision medicine；artificial intelligence

参考文献：

[1] 徐鹏辉. 美国启动精准医疗计划 [J]. 世界复合医学，2015，1（1）：44-46.

[2] BRESHEARS MA. Book Review：Rubin's Pathology：Clinicopathologic Foundations of Medicine[J]. Veterinary Pathology，2008，45（2）：283-283.

[3] Gurcan M N，Boucheron L，Can A，et al. Histopathological Image Analysis：A Review[J]. IEEE Reviews in Biomedical Engineering，2009：147-171.

[4] Dan C. Ciresan，Alessandro Giusti，Luca M. Gambardella，et al. Mitosis Detection in Breast Cancer Histology Images with

Deep Neural Networks [M].Berlin，Heidelberg：Springer Berlin Heidelberg，2013：411-418.

[5] SIRINUKUNWATTANA K，AHMED R S E，YEE-WAH T，et al. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images [J]. IEEETransactions on Medical Imaging，2016，35（5）：1196-1206.

[6] Chen H，Dou Q，Wang X，et al. Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks [C]//Thirtieth AAAI Conference on Artificial Intelligence. AAAI，2016：1160-1166.

[7] Hao Chen，Xiaojuan Qi，Lequan Yu，et al. DCAN：Deep Contour-aware Networks for Accurate Gland Segmentation [C]//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. IEEE，2016：2487-2496.

[8] Ronneberger O，Fischer P，Brox T. U-net：Convolutional Networks for Bomedical Image Segmentation [C]//International Conference on Medical Image Computing and Computer-assisted Intervention. Springer，Cham，2015：234-241.

[9] Xu Y，Li Y，Liu M，et al. Gland Instance Segmentation by Deep Multichannel Side Supervision [J]. IEEE Transactions on Biomedical Engineering，2017，64（12）：2901-2912.

[10] EDGE S B，COMPTON C C. The American Joint Committee on Cancer：the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM[J]. Annals of Surgical Oncology，2010，17（6）：1471-1474.

[11] FANSHAWE TR，LYNCH AG，ELLIS IO，et al. Assessing agreement between multiple raters with missing rating information，applied to breast cancer tumour grading [J]. PloS one，2008，3（8）：e2925.

[12] Ertosun M G，Rubin D L. Automated Grading of Gliomas using Deep Learning in Digital Pathology Images：A Modular Approach with Ensemble of Convolutional Neural Networks [J]. AMIA. Annual Symposium Proceedings，2015：1899.

[13] Spanhol F A，Oliveira L S，Petitjean C，et al. Breast Cancer Histopathological Image Classification using Convolutional Neural Networks [C]//International Joint Conference on Neural Networks.IEEE，2016：2560-2567.

[14] Tao Wan，Jiajia Cao，Jianhui Chen，et al. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features [J]. Neurocomputing，2017，229：34-44.