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

Background: 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.