使用Grounding DINO、YOLO和DINO-DETR评估狭窄检测
Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR
AI总结：
本研究基于MMDetection框架和ARCADE数据集，评估了Grounding DINO、YOLO和DINO-DETR等模型在冠状动脉造影狭窄检测中的性能，为深度学习诊断的模型选择与改进提供了见解。
摘要：
在冠状动脉造影中检测狭窄对于诊断和管理心血管疾病至关重要。本研究使用MMDetection框架评估了最先进的目标检测模型在ARCADE数据集上的性能。采用COCO评估指标对模型进行评估，包括交并比、平均精度（AP）和平均召回率（AR）。结果显示不同模型的检测精度存在差异，这归因于算法设计以及基于Transformer与卷积架构的不同。此外，在实现过程中遇到了一些挑战，如PyTorch、CUDA和MMDetection之间的兼容性问题，以及ARCADE中的数据集不一致性。研究结果为狭窄检测的模型选择提供了见解，并突出了基于深度学习的冠状动脉疾病诊断中需要进一步改进的领域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
