对比三种模型在冠脉造影狭窄检测中的表现，为临床诊断提供算法选型参考。
Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR
- 使用MMDetection框架评测Grounding DINO、YOLO和DINO-DETR的检测性能
- 不同模型在IoU、AP和AR指标上表现差异明显，反映架构设计影响
- 适用于医学影像分析与深度学习模型评估的研究者及临床辅助诊断开发人员
冠状动脉造影中的狭窄检测对心血管疾病诊断与管理至关重要。本研究在ARCADE数据集上，基于MMDetection框架评估了当前先进的目标检测模型性能，采用COCO评价指标（包含交并比IoU、平均精度AP、平均召回率AR）。结果表明，不同模型在检测准确率上存在差异，主要源于算法设计差异，尤其是基于Transformer与卷积架构的对比。此外，实现过程中遇到兼容性问题（如PyTorch、CUDA与MMDetection版本冲突）以及ARCADE数据集内部不一致性等挑战。研究为冠状动脉疾病深度学习诊断中的模型选择提供了依据，并指出了进一步优化的方向。
原文摘要 · Abstract (English)
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.
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