Abstract:
We propose a 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. We want to achieve 97% accuracy by using CCN algorithm in machine
learning.
