Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR
It addresses stenosis detection for cardiovascular disease diagnosis, but is incremental as it focuses on comparing existing models.
This study evaluated state-of-the-art object detection models for detecting stenosis in coronary angiography on the ARCADE dataset, finding variations in detection accuracy across models due to differences in algorithmic design.
Detecting stenosis in coronary angiography is vital for diagnosing and managing cardiovascular diseases. This study evaluates the performance of state-of-the-art object detection models on the ARCADE dataset using the MMDetection framework. The models are assessed using COCO evaluation metrics, including Intersection over Union (IoU), Average Precision (AP), and Average Recall (AR). Results indicate variations in detection accuracy across different models, attributed to differences in algorithmic design, transformer-based vs. convolutional architectures. Additionally, several challenges were encountered during implementation, such as compatibility issues between PyTorch, CUDA, and MMDetection, as well as dataset inconsistencies in ARCADE. The findings provide insights into model selection for stenosis detection and highlight areas for further improvement in deep learning-based coronary artery disease diagnosis.