LCRNet: local cross-channel recalibration network for liver cancer classification based on CT images.

ABSTRACT: Liver cancer is the leading cause of mortality in the world. Over the years, researchers have spent much effort in developing computer-aided techniques to improve clinicians' diagnosis efficiency and precision, aiming at helping patients with liver cancer to take treatment early. Recently, attention mechanisms can enhance the representational power of convolutional neural networks (CNNs), which have been widely used in medical image analysis. In this paper, we propose a novel architectural unit, local cross-channel recalibration (LCR) module, dynamically adjusting the relative importance of intermediate feature maps by considering the roles of different global context features and building the local dependencies between channels. LCR first extracts different global context features and int

SUBMITTER: Fang Q 

PROVIDER: S-EPMC10713501 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature