Papers › Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Simon Graham, David Epstein, Nasir Rajpoot
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural Networks (CNNs), resulting in data hungry models that learn independent features at each orientation. Allowing CNNs to be rotation-equivariant removes the necessity to learn this set of transformations from the data and instead frees up model capacity, allowing more discriminative features to be learned. This reduction in the number of required parameters also reduces the risk of overfitting. In this paper, we propose Dense Steerable Filter CNNs (DSF-CNNs) that use group convolutions with multiple rotated copies of each filter in a densely connected framework. Each filter is defined as a linear combination of steerable basis filters, enabling exact rotation and decreasing the number of trainable parameters compared to standard filters. We also provide the first in-depth comparison of different rotation-equivariant CNNs for histology image analysis and demonstrate the advantage of encoding rotational symmetry into modern architectures. We show that DSF-CNNs achieve state-of-the-art performance, with significantly fewer parameters, when applied to three different tasks in the area of computational pathology: breast tumour classification, colon gland segmentation and multi-tissue nuclear segmentation.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Breast Tumour Classification | PCam | DSF-CNN (C8) | AUC | 0.975 | #1 of 16 | Archive leaderboard | report |
| Colorectal Gland Segmentation: | CRAG | DSF-CNN (C8) | Dice | 0.891 | #2 of 15 | Archive leaderboard | report |
| Colorectal Gland Segmentation: | CRAG | DSF-CNN (C8) | F1-score | 0.874 | #2 of 15 | Archive leaderboard | report |
| Colorectal Gland Segmentation: | CRAG | DSF-CNN (C8) | Hausdorff Distance (mm) | 138.4 | #2 of 15 | Archive leaderboard | report |
| Multi-tissue Nucleus Segmentation | Kumar | DSF-CNN (C8) | Dice | 0.826 | #2 of 18 | Archive leaderboard | report |
| Multi-tissue Nucleus Segmentation | Kumar | DSF-CNN (C8) | Hausdorff Distance (mm) | 60 | #2 of 18 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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