论文标题
从整个幻灯片图像中提取Gleason组织和分级前列腺癌的扩张的残留分层分割框架
A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images
论文作者
论文摘要
前列腺癌（PCA）是男性中第二个最致命的癌症形式，可以通过检查Gleason组织的结构表现来在临床上分级。本文提出了\ rv {一种新方法}，用于分割格里森组织\ rv {（patch-wise），以便从整个幻灯片图像中对PCA进行评分（WSI）。}另外，所提出的方法涵盖了两个主要贡献：1）杂交因子和层次范围的型号的协调性损坏，并具有三个阶级的分解，并有效地代表有效的型号，有效地代表了有效性的有效型号，有序有效地代表了有效性的有效型号。惩罚不同的语义分割模型，以准确提取高度相关的模式。除此之外，在包含10,516次全滑扫描的大规模PCA数据集上进行了广泛的评估（约7170万个补丁），在该数据集上，它的最终方案超过了3.22％的最终方案（在平均跨越跨间距方面）的进步和6.91％（以6.91％的范围）（以平均交流为单位）（以平均交流为单位）。
Prostate cancer (PCa) is the second deadliest form of cancer in males, and it can be clinically graded by examining the structural representations of Gleason tissues. This paper proposes \RV{a new method} for segmenting the Gleason tissues \RV{(patch-wise) in order to grade PCa from the whole slide images (WSI).} Also, the proposed approach encompasses two main contributions: 1) A synergy of hybrid dilation factors and hierarchical decomposition of latent space representation for effective Gleason tissues extraction, and 2) A three-tiered loss function which can penalize different semantic segmentation models for accurately extracting the highly correlated patterns. In addition to this, the proposed framework has been extensively evaluated on a large-scale PCa dataset containing 10,516 whole slide scans (with around 71.7M patches), where it outperforms state-of-the-art schemes by 3.22% (in terms of mean intersection-over-union) for extracting the Gleason tissues and 6.91% (in terms of F1 score) for grading the progression of PCa.
