当前位置: X-MOL 学术Pattern Recogn. › 论文详情
HscoreNet: A Deep Network for Estrogen and Progesterone Scoring Using Breast IHC Images
Pattern Recognition ( IF 5.898 ) Pub Date : 2020-01-10 , DOI: 10.1016/j.patcog.2020.107200
Monjoy Saha; Indu Arun; Rosina Ahmed; Sanjoy Chatterjee; Chandan Chakraborty

Estrogen and progesterone receptors serve as an important predictive and prognostic biomarkers for breast cancer immunohistological analysis. For breast cancer prognosis, pathologists manually compute the score based on the visual expression and the number of immunopositive and immunonegative nuclei. This manual scoring technique is time-consuming, cumbersome, expensive, error-prone, and susceptible to intra- and interobserver ambiguities. To solve these issues, we proposed a deep neural network (i.e., HscoreNet), which consists of three parts, i.e., encoder, decoder, and scoring layer. A total of 600 (300 ER and 300 PR) regions of interest at 40 × magnification from 100 histologically confirmed slides were used in this study. The size of each region of interest was 2048 × 1536 pixels (width × height). The encoder layer has been used to transform input pixels into a lower-dimensional representation, whereas the decoder reconstructs the output of the encoder through minimization of a cost function. The decoder generates an image that only contains immunopositive and immunonegative nuclei. The output of the decoder is fed to the input of the scoring layer. This layer computes the Histo-score or H-score based on the staining intensity, the color expression, and the number of immunopositive and immunonegative nuclei. Pathologists compute this score to subcategorize the cancer grades and to decide proper treatment procedures. Our proposed approach is affordable, accurate, and fast. We achieved excellent performance, with 95.87% precision and 94.53% classification accuracy. Our proposed approach streamlines the human error-prone and time-consuming process. This methodology can also be used for other types of histology and immunohistology image segmentation and scoring.
更新日期:2020-01-11

 

全部期刊列表>>
化学/材料学中国作者研究精选
Springer Nature 2019高下载量文章和章节
《科学报告》最新环境科学研究
ACS材料视界
自然科研论文编辑服务
中南大学国家杰青杨华明
剑桥大学-
中国科学院大学化学科学学院
材料化学和生物传感方向博士后招聘
课题组网站
X-MOL
北京大学分子工程苏南研究院
华东师范大学分子机器及功能材料
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug