SRE-Conv: Symmetric Rotation Equivariant Convolution for Biomedical Image Classification
Journal:
arXiv
Published Date:
Jan 16, 2025
Abstract
Convolutional neural networks (CNNs) are essential tools for computer vision
tasks, but they lack traditionally desired properties of extracted features
that could further improve model performance, e.g., rotational equivariance.
Such properties are ubiquitous in biomedical images, which often lack explicit
orientation. While current work largely relies on data augmentation or explicit
modules to capture orientation information, this comes at the expense of
increased training costs or ineffective approximations of the desired
equivariance. To overcome these challenges, we propose a novel and efficient
implementation of the Symmetric Rotation-Equivariant (SRE) Convolution
(SRE-Conv) kernel, designed to learn rotation-invariant features while
simultaneously compressing the model size. The SRE-Conv kernel can easily be
incorporated into any CNN backbone. We validate the ability of a deep SRE-CNN
to capture equivariance to rotation using the public MedMNISTv2 dataset (16
total tasks). SRE-Conv-CNN demonstrated improved rotated image classification
performance accuracy on all 16 test datasets in both 2D and 3D images, all
while increasing efficiency with fewer parameters and reduced memory footprint.
The code is available at https://github.com/XYPB/SRE-Conv.
