LoG-staging: a rectal cancer staging method with LoG operator based on maximization of mutual information.

ABSTRACT: Deep learning methods have been migrated to rectal cancer staging as a classification process based on magnetic resonance images (MRIs). Typical approaches suffer from the imperceptible variation of images from different stage. The data augmentation also introduces scale invariance and rotation consistency problems after converting MRIs to 2D visible images. Moreover, the correctly labeled images are inadequate since T-staging requires pathological examination for confirmation. It is difficult for classification model to characterize the distinguishable features with limited labeled data. In this article, Laplace of Gaussian (LoG) filter is used to enhance the texture details of converted MRIs and we propose a new method named LoG-staging to predict the T stages of rectal cancer patients.

SUBMITTER: Zhang G 

PROVIDER: S-EPMC11887235 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature