COVID-19 severity detection using chest X-ray segmentation and deep learning Article Open access 27 August 2024 Introduction The rapid pandemic-level outbreak of coronavirus disease 2019 (COVID-19) has caused a wide range and degree of illnesses, predominated by respiratory tract infection1,2,3...
medical-imagingmedical-image-processinglung-segmentationmedical-image-analysischest-ctlung-diseasecovid-19lung-lobescovid-19-ct UpdatedApr 6, 2024 Python 天池医疗AI大赛[第一季]:肺部结节智能诊断 UNet/VGG/Inception/ResNet/DenseNet neural-networkkerasscikit-imagevggclassificationlung-cancer-detectionsegmentatio...
Detection and segmentation of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV2 or COVID-19) is a difficult task due the different kinds of shapes... A Salazar-Urbina,E Ventura-Molina,MLYI Yanez-Marquez - 《Computacion Y Sistemas》 被引量: 0发表: 2024年 加载更多来源...
a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attenti...
为了应对这些挑战,提出了一种新型的 COVID-19 肺部感染分割深度网络(Inf-Net)来自动从胸部 CT 切片中识别感染区域。在我们的 Inf-Net 中,并行部分解码器用于聚合高级特征并生成全局图。然后,利用隐式反向注意和显式边缘注意对边界进行建模并增强表示。此外,为了缓解标记数据的短缺,我们提出了一种基于随机选择的传播...
Extensive experiments on our COVIDSemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance. 展开 关键词: COVID-19 CT image infection segmentation semi-supervised learning ...
在COVID-SemiSeg和真实CT体积上进行广泛实验表明,Inf-Net性能优于大多数尖端分割模型,并提高了当前的性能水平。 作者指出其动机源于临床医生在肺部感染检测过程中,首先对感染区域进行粗略定位,然后根据局部症状准确提取其轮廓。因此,我们认为区域和边界是区分正常组织和感染的两个关键特征。因此,我们的网络首先预测粗糙区...
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infe... DP Fan,T Zhou,GP Ji,... - 《IEEE Transactions on Medical ...
Artificial Intelligence (AI) can play a key role in enhancing COVID detection. However, lung infection by COVID is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID...
Transfer learning is also widely used to complement the sample size issue in COVID-19 infection detection and segmentation from medical images [19,18,21]. However, transfer learning usually involves models pretrained on non-medical images, which may not perform well in the medical image scenario....