We propose a new model for digital pathology segmentation, based on the
observation that histopathology images are inherently symmetric under rotation
and reflection. Utilizing recent findings on rotation equivariant CNNs, the
proposed model leverages these symmetries in a principled manner. We present a
visual analysis showing improved stability on predictions, and demonstrate that
exploiting rotation equivariance significantly improves tumor detection
performance on a challenging lymph node metastases dataset. We further present
a novel derived dataset to enable principled comparison of machine learning
models, in combination with an initial benchmark. Through this dataset, the
task of histopathology diagnosis becomes accessible as a challenging benchmark
for fundamental machine learning research.

基于近期对旋转等变 CNN 的发现，我们提出了一种新的数字病理学分割模型，利用组织病理学图像在旋转和镜像下的固有对称性，在合理的方式下提高了肿瘤检测性能，并通过建立的衍生数据集和初始基准，将组织病理学诊断作为一个挑战的基准测试，以便进行基础机器学习研究的比较。