Histopathological assessments, including surgical resection and core needle
biopsy, are the standard procedures in the diagnosis of the prostate cancer.
Current interpretation of the histopathology images includes the determination
of the tumor area, Gleason grading, and identification of certain
prognosis-critical features. Such a process is not only tedious, but also prune
to intra/inter-observe variabilities. Recently, FDA cleared the marketing of
the first whole slide imaging system for digital pathology. This opens a new
era for the computer aided prostate image analysis and feature extraction based
on the digital histopathology images. In this work, we present an analysis
pipeline that includes localization of the cancer region, grading, area ratio
of different Gleason grades, and cytological/architectural feature extraction.
The proposed algorithm combines the human engineered feature extraction as well
as those learned by the deep neural network. Moreover, the entire pipeline is
implemented to directly operate on the whole slide images produced by the
digital scanners and is therefore potentially easy to translate into clinical
practices. The algorithm is tested on 368 whole slide images from the TCGA data
set and achieves an overall accuracy of 75% in differentiating Gleason 3+4 with
4+3 slides.

本文介绍了一种结合人工和深度神经网络特征提取的分析流程，并直接操作于数字扫描仪所生成的全幅图像上，实现了前列腺癌癌区定位、分级、提取 Gleason 分级的面积比率和细胞 / 结构特征，最终通过对 368 个全幅图像进行测试，成功在區分 Gleason 3+4 和 4+3 的幻灯片方面达到了 75％的总体准确性。