A Computationally Virtual Histological Staining Method to Ovarian Cancer Tissue by Deep Generative Adversarial Networks

doi:10.1155/2021/4244157

	A Computationally Virtual Histological Staining Method to Ovarian Cancer Tissue by Deep Generative Adversarial Networks
	Meng, Xiangyu 1,2; Li, Xin 3; Wang, Xun 1,4
刊名	COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
	2021-07-02
卷号	2021 页码:12
ISSN号	1748-670X
DOI	10.1155/2021/4244157
英文摘要	Histological analysis to tissue samples is elemental for diagnosing the risk and severity of ovarian cancer. The commonly used Hematoxylin and Eosin (H&E) staining method involves complex steps and strict requirements, which would seriously impact the research of histological analysis of the ovarian cancer. Virtual histological staining by the Generative Adversarial Network (GAN) provides a feasible way for these problems, yet it is still a challenge of using deep learning technology since the amounts of data available are quite limited for training. Based on the idea of GAN, we propose a weakly supervised learning method to generate autofluorescence images of unstained ovarian tissue sections corresponding to H&E staining sections of ovarian tissue. Using the above method, we constructed the supervision conditions for the virtual staining process, which makes the image quality synthesized in the subsequent virtual staining stage more perfect. Through the doctors' evaluation of our results, the accuracy of ovarian cancer unstained fluorescence image generated by our method reached 93%. At the same time, we evaluated the image quality of the generated images, where the FID reached 175.969, the IS score reached 1.311, and the MS reached 0.717. Based on the image-to-image translation method, we use the data set constructed in the previous step to implement a virtual staining method that is accurate to tissue cells. The accuracy of staining through the doctor's assessment reached 97%. At the same time, the accuracy of visual evaluation based on deep learning reached 95%.
资助项目	National Natural Science Foundation of China[61972416] ; National Natural Science Foundation of China[61873280] ; National Natural Science Foundation of China[61873281] ; Natural Science Foundation of Shandong Province[ZR2019MF012]
WOS研究方向	Mathematical & Computational Biology
语种	英语
出版者	HINDAWI LTD
WOS记录号	WOS:000674569200002
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/17466]
专题	中国科学院计算技术研究所
通讯作者	Li, Xin; Wang, Xun
作者单位	1.China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Shandong, Peoples R China 2.Inner Mongolia Agr Univ, Coll Comp & Informat Sci, Hohhot 010018, Inner Mongolia, Peoples R China 3.Wuhan Univ, Dept Gynecol 2, Renmin Hosp, Wuhan 430060, Hubei, Peoples R China 4.Chinese Acad Sci, China High Performance Comp Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Meng, Xiangyu,Li, Xin,Wang, Xun. A Computationally Virtual Histological Staining Method to Ovarian Cancer Tissue by Deep Generative Adversarial Networks[J]. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE,2021,2021:12.
APA	Meng, Xiangyu,Li, Xin,&Wang, Xun.(2021).A Computationally Virtual Histological Staining Method to Ovarian Cancer Tissue by Deep Generative Adversarial Networks.COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE,2021,12.
MLA	Meng, Xiangyu,et al."A Computationally Virtual Histological Staining Method to Ovarian Cancer Tissue by Deep Generative Adversarial Networks".COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021(2021):12.