Ansari M, Bhat S, Maier A (2025)
Publication Language: English
Publication Status: Published
Publication Type: Other publication type
Future Publication Type: Other publication type
Publication year: 2025
Open Access Link: https://arxiv.org/abs/2503.01601
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
APA:
Ansari, M., Bhat, S., & Maier, A. (2025). Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR.
MLA:
Ansari, Musab, Sheethal Bhat, and Andreas Maier. Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR. 2025.
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