提出新型旋转等变卷积，提升生物医学图像分类精度与效率
SRE-Conv: Symmetric Rotation Equivariant Convolution for Biomedical Image Classification
- 设计对称旋转等变卷积核，无需依赖数据增强
- 在16个公开数据集上实现全任务精度提升，参数更少
- 可无缝嵌入任意CNN，适合医疗图像分析场景
卷积神经网络是计算机视觉的重要工具，但传统方法缺乏旋转等变性等理想特征属性，而这类性质在生物医学图像中普遍存在，因图像常无明确方向。现有方法多依赖数据增强或显式模块捕捉方向信息，带来训练成本增加或近似效果不佳的问题。为此，本文提出一种新型高效的对称旋转等变卷积（SRE-Conv）核，可在学习旋转不变特征的同时压缩模型规模。该卷积核可轻松集成至任意CNN主干网络。通过公共MedMNISTv2数据集（共16个任务）验证，深度SRE-CNN能有效捕获旋转等变性，在2D和3D图像上所有16个测试数据集均实现分类精度提升，且参数更少、内存占用更低。代码已开源。
原文摘要 · Abstract (English)
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.
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