Flattening Singular Values of Factorized Convolution for Medical Images

arXiv:2403.00606v1 Announce Type: new
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 …

abstract arxiv capabilities cnns computer computing convolution convolutional neural networks cs.cv deploy devices diagnosis image image processing images medical networks neural networks paradigm processing robust singular support type values