论文标题
Pother：用于贴片的深度学习的胸部X射线偏置分析，用于COVID-19检测
POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detection
论文作者
论文摘要
在与Covid-19的斗争中，对人们的生活产生灾难性影响，这是对患有严重COVID-19症状的诊所中介绍的患者的有效筛查。胸部射线照相是有前途的筛选方法之一。许多研究报告说，使用深度学习准确地检测到胸部X射线射线的Covid-19。对许多已发表的方法的严重局限性不足以解释深度学习模型做出的决策。使用可解释的人工智能方法，我们证明模型决策可能依赖于混杂因素而不是医学病理学。在分析了在胸部X射线图像上发现的潜在混杂因素后，我们提出了一种新颖的方法来最大程度地减少其负面影响。我们表明，我们所提出的方法比以前试图抵抗混杂因素（例如ECG铅X射线中经常影响模型分类决策）更强大。除了强大之外，我们的方法还取得了与最先进的结果相媲美的结果。源代码和预训练的权重可在（https://github.com/tomek1911/pother）上公开获得。
A critical step in the fight against COVID-19, which continues to have a catastrophic impact on peoples lives, is the effective screening of patients presented in the clinics with severe COVID-19 symptoms. Chest radiography is one of the promising screening approaches. Many studies reported detecting COVID-19 in chest X-rays accurately using deep learning. A serious limitation of many published approaches is insufficient attention paid to explaining decisions made by deep learning models. Using explainable artificial intelligence methods, we demonstrate that model decisions may rely on confounding factors rather than medical pathology. After an analysis of potential confounding factors found on chest X-ray images, we propose a novel method to minimise their negative impact. We show that our proposed method is more robust than previous attempts to counter confounding factors such as ECG leads in chest X-rays that often influence model classification decisions. In addition to being robust, our method achieves results comparable to the state-of-the-art. The source code and pre-trained weights are publicly available at (https://github.com/tomek1911/POTHER).
