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

## TL;DR

This paper introduces LoG-staging, a new method for predicting rectal cancer stages using MRI images with improved accuracy through texture enhancement and feature clustering.

## Contribution

The novel LoG-staging method combines LoG filtering and mutual information maximization to address limited labeled data and improve rectal cancer staging accuracy.

## Key findings

- LoG-staging outperforms nonlinear dimensionality reduction in predicting rectal cancer T stages.
- The proposed method compensates for limited labeled data using feature clustering and mutual information maximization.
- Texture enhancement with LoG filter improves boundary clarity in rectal cancer MRI images.

## 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. We first use the LoG operator to clarify the fuzzy boundaries of rectal cancer cell proliferation. Then, we propose a new feature clustering method by leveraging the maximization of mutual information (MMI) mechanism which jointly learns the parameters of a neural network and the cluster assignments of features. The assignments are used as labels for the next round of training, which compensate the inadequacy of labeled training data. Finally, we experimentally verify that the LoG-staging is more accurate than the nonlinear dimensionality reduction in predicting the T stages of rectal cancer. We innovatively implement information bottleneck (IB) method in T-staging of rectal cancer based on image classification and impressive results are obtained.

The online version contains supplementary material available at 10.1186/s12880-025-01610-7.