COVID-Net USPro：一种开源的可解释少样本深度原型网络，用于从点即服务超声图像中监测和检测新冠病毒感染
COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images
- University of Waterloo（滑铁卢大学）
- National Research Council Canada（加拿大国家研究委员会）
- McGill University（麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
COVID-Net USPro 是一种基于深度学习的可解释少样本原型网络，用于从点即服务超声图像中高精度检测 COVID-19 阳性病例。
AI中文摘要：
随着新冠病毒疾病2019（COVID-19）持续影响生活的各个方面和全球医疗系统，采用快速有效的筛查方法以防止病毒进一步传播并减轻医疗提供者的负担已成为必要。作为一种廉价且广泛可用的医学图像模态，点即服务超声（POCUS）成像允许放射科医生通过检查胸部超声图像来识别症状并评估严重程度。结合最近的计算机科学进展，深度学习技术在医学图像分析中的应用已显示出有希望的结果，证明了基于人工智能的解决方案可以加速对 COVID-19 的诊断并减轻医疗专业人员的负担。然而，缺乏大量良好标注的数据在构建有效深度神经网络时对新型疾病和流行病构成挑战。受此启发，我们提出了 COVID-Net USPro，一种可解释的少样本深度原型网络，能够从极少量的超声图像中以高精度和召回率监测和检测 COVID-19 阳性病例。当仅使用5个样本进行训练时，COVID-Net USPro 在 COVID-19 阳性病例上实现了99.65%的总体准确率、99.7%的召回率和99.67%的精确率。分析流程和结果由具有广泛POCUS解读经验的贡献临床医生验证，确保网络基于实际模式做出决策。
英文摘要：
As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and assess severity through visual inspection of the chest ultrasound images. Combined with the recent advancements in computer science, applications of deep learning techniques in medical image analysis have shown promising results, demonstrating that artificial intelligence-based solutions can accelerate the diagnosis of COVID-19 and lower the burden on healthcare professionals. However, the lack of a huge amount of well-annotated data poses a challenge in building effective deep neural networks in the case of novel diseases and pandemics. Motivated by this, we present COVID-Net USPro, an explainable few-shot deep prototypical network, that monitors and detects COVID-19 positive cases with high precision and recall from minimal ultrasound images. COVID-Net USPro achieves 99.65% overall accuracy, 99.7% recall and 99.67% precision for COVID-19 positive cases when trained with only 5 shots. The analytic pipeline and results were verified by our contributing clinician with extensive experience in POCUS interpretation, ensuring that the network makes decisions based on actual patterns.
