Improving automated analysis of medical imaging will provide clinicians more options in providing care for patients. The 2023 AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D) provides an opportunity to test and refine machine learning methods for detecting the presence and severity of COVID-19 in patients from CT scans. This paper presents version 2 of Cov3d, a deep learning model submitted in the 2022 competition. The model has been improved through a preprocessing step which segments the lungs in the CT scan and crops the input to this region. It results in a validation macro F1 score for predicting the presence of COVID-19 in the CT scans at 92.2% which is significantly above the baseline of 74%. It gives a macro F1 score for predicting the severity of COVID-19 on the validation set for task 2 as 67% which is above the baseline of 38%.
翻译:改进医疗成像的自动化分析将为临床医生提供更多的治疗选择。2023年由AI支持的医学图像分析讲习班和Covid-19诊断竞赛(AI-MIA-COV19D)为测试和完善检测CT扫描病人COVID-19的存在和严重程度的机器学习方法提供了一个机会。本文介绍了2022年竞赛中提交的Cov3d第二版的深层学习模式Cov3d。该模型通过一个预处理步骤得到了改进,该预处理步骤将肺部分解到CT扫描中,并将输入到这个区域。它使预测CT扫描中COVID-19的存在达到92.2%的鉴定宏观F1分,大大高于74%的基线。它为预测任务2的鉴定标准COVID-19的严重程度提供了宏观F1分,预测任务2的COVID-19的强度为67%,高于38%的基线。</s>