改进的RPA架构让口腔癌病变检测速度提升60-100倍
Novel Architecture of RPA In Oral Cancer Lesion Detection
- 采用单例模式与批量处理设计提升推理效率
- 新架构每图仅需0.06秒，较传统方法提速60-100倍
- 适合临床快速筛查场景，降低检测成本
准确且早期发现口腔癌病灶对诊断和治疗至关重要。本研究评估了两种RPA实现方式：OC-RPAv1与OC-RPAv2，使用31张图像组成的测试集进行验证。OC-RPAv1每张图像平均耗时0.29秒；而OC-RPAv2采用单例设计模式与批量处理，将每图预测时间降至0.06秒。相比标准RPA方法，该方案效率提升60至100倍，证明设计模式与批量处理能显著增强口腔癌检测的可扩展性并降低成本。
原文摘要 · Abstract (English)
Accurate and early detection of oral cancer lesions is crucial for effective diagnosis and treatment. This study evaluates two RPA implementations, OC-RPAv1 and OC-RPAv2, using a test set of 31 images. OC-RPAv1 processes one image per prediction in an average of 0.29 seconds, while OCRPAv2 employs a Singleton design pattern and batch processing, reducing prediction time to just 0.06 seconds per image. This represents a 60-100x efficiency improvement over standard RPA methods, showcasing that design patterns and batch processing can enhance scalability and reduce costs in oral cancer detection
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