CAAFE-ResNet: A ResNet With Channel Attention-Augmented Feature Extraction for Prognostic Assessment in Rectal Cancer

Abstract

Magnetic resonance imaging (MRI) has a pivotal role in both pretreatment staging and post-treatment evaluation of rectal cancer. This study presents an innovative deep learning model, CAAFE-ResNet18*, based on the residual neural network ResNet18*. The model features an ingeniously designed feature extraction and complementation module (i.e., CAAFE), which leverages a multiscale dilated convolution parallel architecture combined with a channel attention mechanism (CAM) to achieve multilevel information fusion, spatial feature enhancement and channel feature optimisation. This enables in-depth exploration and augmentation of multilevel downsampled features, significantly improving feature representation capability and overall performance. Testing on rectal cancer MRI data demonstrates that the CAAFE-ResNet18* model significantly outperforms convolutional neural network (CNN) backbone networks and recent state-of-the-art (SOTA) models. This result indicates that the CAAFE model, by complementing and extracting MR images of patients with locally advanced rectal cancer (LARC) features at different scales from ResNet18*, may help to identify patients who will show complete response (CR) at the end of treatment and those who will not respond to therapy (NR) at an early stage of the treatment.