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Flattening Singular Values of Factorized Convolution for Medical Images

2024-03-01 15:30:50
Zexin Feng, Na Zeng, Jiansheng Fang, Xingyue Wang, Xiaoxi Lu, Heng Meng, Jiang Liu

Abstract

Convolutional neural networks (CNNs) have long been the paradigm of choice for robust medical image processing (MIP). Therefore, it is crucial to effectively and efficiently deploy CNNs on devices with different computing capabilities to support computer-aided diagnosis. Many methods employ factorized convolutional layers to alleviate the burden of limited computational resources at the expense of expressiveness. To this end, given weak medical image-driven CNN model optimization, a Singular value equalization generalizer-induced Factorized Convolution (SFConv) is proposed to improve the expressive power of factorized convolutions in MIP models. We first decompose the weight matrix of convolutional filters into two low-rank matrices to achieve model reduction. Then minimize the KL divergence between the two low-rank weight matrices and the uniform distribution, thereby reducing the number of singular value directions with significant variance. Extensive experiments on fundus and OCTA datasets demonstrate that our SFConv yields competitive expressiveness over vanilla convolutions while reducing complexity.

Abstract (translated)

卷积神经网络(CNNs)一直是用于稳健医疗图像处理(MIP)的范式。因此,在部署具有不同计算能力的设备上的CNN至关重要,以支持计算机辅助诊断。许多方法采用因子化卷积层来减轻有限计算资源带来的压力,但牺牲了表达力。为此,在弱医学图像驱动的CNN模型优化方面,我们提出了一个奇异值等价广义卷积(SFConv)来提高离散卷积在MIP模型中的表现。我们首先将卷积滤波器的重量矩阵分解为两个低秩矩阵以实现模型压缩。然后,在两个低秩权重矩阵和均匀分布之间最小化KL散度,从而降低具有较大方差的不稳定奇异值方向的数量。在断层和OCTA数据集上的大量实验证明,与普通卷积相比,我们的SFConv具有竞争力的表现,同时减少了复杂性。

URL

https://arxiv.org/abs/2403.00606

PDF

https://arxiv.org/pdf/2403.00606.pdf


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