轻量模型精准识别腹主动脉钙化，助力心血管病早期预警
AACLiteNet: A Lightweight Model for Detection of Fine-Grained Abdominal Aortic Calcification
- 设计轻量化网络，同时预测钙化总分与局部评分
- 准确率达85.94%，计算成本降低19.88倍，内存减少2.26倍
- 适合部署在移动设备，提升基层筛查可及性
心血管疾病（CVD）是全球每年导致1790万人死亡的首要原因。腹主动脉钙化（AAC）是心血管疾病的可靠标志，可在侧位椎体骨折评估（VFA）扫描中观察到，通常用于椎体骨折检测。早期发现AAC有助于通过预防措施降低临床心血管病风险。手动分析VFA扫描进行AAC评估耗时且需专业人员。尽管已有自动化尝试，但现有模型或精度低、缺乏细粒度评分预测，或计算和内存开销过大。为此，本文提出轻量级深度学习模型AACLiteNet，可高精度预测累计与细粒度级别的AAC评分，同时具备低内存占用和低计算成本（浮点运算次数，FLOPs）。相比之前最佳模型（81.98%准确率），本模型在一对多平均准确率上达到85.94%，计算成本降低19.88倍，内存占用减少2.26倍，适用于便携式计算设备部署。
原文摘要 · Abstract (English)
Cardiovascular Diseases (CVDs) are the leading cause of death worldwide, taking 17.9 million lives annually. Abdominal Aortic Calcification (AAC) is an established marker for CVD, which can be observed in lateral view Vertebral Fracture Assessment (VFA) scans, usually done for vertebral fracture detection. Early detection of AAC may help reduce the risk of developing clinical CVDs by encouraging preventive measures. Manual analysis of VFA scans for AAC measurement is time consuming and requires trained human assessors. Recently, efforts have been made to automate the process, however, the proposed models are either low in accuracy, lack granular level score prediction, or are too heavy in terms of inference time and memory footprint. Considering all these shortcomings of existing algorithms, we propose 'AACLiteNet', a lightweight deep learning model that predicts both cumulative and granular level AAC scores with high accuracy, and also has a low memory footprint, and computation cost (Floating Point Operations (FLOPs)). The AACLiteNet achieves a significantly improved one-vs-rest average accuracy of 85.94% as compared to the previous best 81.98%, with 19.88 times less computational cost and 2.26 times less memory footprint, making it implementable on portable computing devices.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。