# Rotation Equivariant CNNs for Digital Pathology

arXiv: https://arxiv.org/abs/1806.03962 · HTML: https://mlsanity.com/paper/1806.03962/

## Key facts

- Cited by 422 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

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## Abstract (arXiv)

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.