论文标题
与病理学家一级表现在乳腺癌中的自动评分的自动评分
Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer
论文作者
论文摘要
本文定义为肿瘤核整体外观异常的程度，是三层乳腺癌分级的组成部分之一。鉴于核多态性反映了连续的变化范围，我们从几位病理学家的集体知识中训练了多种肿瘤区域的深神经网络，而没有将网络限制为传统的三类分类。我们还激发了一种额外的方法，在该方法下，我们讨论了正常上皮作为基线的额外益处，遵循常规的临床实践，病理学家接受了培训以在肿瘤中对核多态性进行评分，并具有正常的乳房上皮脂质以进行比较。在多个实验中，与十个和四个病理学家相比，我们完全自动化的方法可以在某些感兴趣的区域和整个幻灯片图像中实现最高病理学家级的表现。
Nuclear pleomorphism, defined herein as the extent of abnormalities in the overall appearance of tumor nuclei, is one of the components of the three-tiered breast cancer grading. Given that nuclear pleomorphism reflects a continuous spectrum of variation, we trained a deep neural network on a large variety of tumor regions from the collective knowledge of several pathologists, without constraining the network to the traditional three-category classification. We also motivate an additional approach in which we discuss the additional benefit of normal epithelium as baseline, following the routine clinical practice where pathologists are trained to score nuclear pleomorphism in tumor, having the normal breast epithelium for comparison. In multiple experiments, our fully-automated approach could achieve top pathologist-level performance in select regions of interest as well as at whole slide images, compared to ten and four pathologists, respectively.
