MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification.

The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to distinguish between them and affects radiologists' diagnosis. Recently, deep learning in COVID-19 has been mainly divided into disease classification and lesion segmentation, yet little work has focused on the feature correlation between the two tasks. To address these issues, in this study, we propose MultiR-Net, a 3D deep learning model for combined COVID-19 classification and lesion segmentation, to achieve real-time and interpretable COVID-19 chest CT diagnosis. Precisely, the proposed network consists of two subnets: a multi-scale feature fusion UNet-like subnet for lesion segmentation and a classification subnet for disease diagnosis. The features between the two subnets are fused by the reverse attention mechanism and the iterable training strategy. Meanwhile, we proposed a loss function to enhance the interaction between the two subnets. Individual metrics can not wholly reflect network effectiveness. Thus we quantify the segmentation results with various evaluation metrics such as average surface distance, volume Dice, and test on the dataset. We employ a dataset containing 275 3D CT scans for classifying COVID-19, Community-acquired Pneumonia (CAP), and healthy people and segmented lesions in pneumonia patients. We split the dataset into 70% and 30% for training and testing. Extensive experiments showed that our multi-task model framework obtained an average recall of 93.323%, an average precision of 94.005% on the classification test set, and a 69.95% Volume Dice score on the segmentation test set of our dataset.

[Determination of trace elements in traditional Chinese medicine from Changbai Mountain by ICP-MS].

The effects of different digestives for the fritillaria and atractylodes were compared. Many trace elements in the planted and wild fritillaria and atractylodes were determined by ICP-MS The results show that the RSD and recovery are better if the planted and wild fritillaria and atractylodes were digested with HNO3-H2O2. Among the many elements determined from the fritillaria and atractylodes, Cu, Zn, Fe, Mg and Mn are the dominant chemicals. The content of Fe was higher in the wild fritillaria and atractylodes than that in the planted fritillaria and atractylodes, while the contents of heavy metal Pb and Cd were lower in the wild fritillaria and atractylodes than those in the planted fritillaria and atractylodes. The wild fritillaria and atractylodes contain Co, which was not determined in the planted fritillaria and atractylodes. The experimental results showed that the detection limits were lower than 0.086 ng x g(-1) with low RSD(n = 7, 4.85%) for most metal chemicals determined, and the standard recoveries (n = 7) ranged from 96.8 to 103.4%.