DDUM: Deformable Dilated U-structure Module for coronary stenosis detection

Abstract

Deep learning methods are increasingly popular in assisting physicians with diagnosing coronary artery disease and reducing errors caused by subjective judgment. However, accessing and labeling medical imaging data, especially coronary angiography data, is challenging. Consequently, models trained on such datasets often exhibit low accuracy, high false-positive rates, and limited generalization capabilities. We propose a Deformable Dilatable U-structure Module that can specialize a common network for coronary stenosis detection and enhance its generalization ability. Experiments demonstrate that our proposed module significantly improves the performance of various models. When applying DDUM to a model with ResNet50 as the backbone and faster R-CNN as the detector, the mean average precision increases from 33.76 to 42.39, a 25.56% improvement. Additionally, we show that DDUM enhances the network's generalization ability through transfer learning experiments. This module can improve the network's accuracy for stenosis detection and enhance the generalization ability of the original model. Fine-tuning reduces training costs and ensures that the model can be easily adapted and deployed across different devices.