Attentive feature interaction based persistent homology-augmented network for esophageal cancer lesion detection

Detecting lesions in esophageal cancer (EC) is important in guiding subsequent treatment. Deep learning methods based on convolutional neural networks (CNNs) and vision transformer (ViT) have made remarkable strides in the field of medical image analysis due to their powerful representational capabilities. However, without prior knowledge, both traditional CNNs and ViTs are susceptible to disregarding critical anatomical information, including loops and voids. The current methods, combined with persistent homology (PH), have been proposed to address certain limitations, but they neglect the inconsistencies among features caused by the lack of interaction between features, ultimately leading to a reduction in the model's generalization ability.

To address these challenges, we propose a novel framework, combined with PH and feature interaction, for identifying EC lesions from 3D CT images. The goal is to enhance the predictive capability of existing deep learning models by incorporating both topological information from PH and effective feature interaction mechanisms.

We applied cube-wise classification techniques to improve the detection of lesions associated with EC. The proposed framework consists of two fundamental modules: (1) persistence diagram cross-attention encoder (PDCAE) that completely encodes the persistence diagram (PD) created by PH through cross-attention. (2) recalibration guidance module (RGM) connecting the PH features with the image features efficiently to remove inconsistencies.

The experimental results show that the proposed modules significantly enhance the predictive capability of standard backbone networks, and outperform the state-of-the-art classification network.

This work highlights the potential of combining topological data analysis with deep learning for medical image analysis tasks. More potential downstream tasks that can utilize topological relationships remain to be explored in the future.