SDS-Net: A lightweight 3D convolutional neural network with multi-branch attention for multimodal brain tumor accurate segmentation.

The accurate and fast segmentation method of tumor regions in brain Magnetic Resonance Imaging (MRI) is significant for clinical diagnosis, treatment and monitoring, given the aggressive and high mortality rate of brain tumors. However, due to the limitation of computational complexity, convolutional neural networks (CNNs) face challenges in being efficiently deployed on resource-limited devices, which restricts their popularity in practical medical applications. To address this issue, we propose a lightweight and efficient 3D convolutional neural network SDS-Net for multimodal brain tumor MRI image segmentation. SDS-Net combines depthwise separable convolution and traditional convolution to construct the 3D lightweight backbone blocks, lightweight feature extraction (LFE) and lightweight feature fusion (LFF) modules, which effectively utilizes the rich local features in multimodal images and enhances the segmentation performance of sub-tumor regions. In addition, 3D shuffle attention (SA) and 3D self-ensemble (SE) modules are incorporated into the encoder and decoder of the network. The SA helps to capture high-quality spatial and channel features from the modalities, and the SE acquires more refined edge features by gathering information from each layer. The proposed SDS-Net was validated on the BRATS datasets. The Dice coefficients were achieved 92.7, 80.0 and 88.9% for whole tumor (WT), enhancing tumor (ET) and tumor core (TC), respectively, on the BRTAS 2020 dataset. On the BRTAS 2021 dataset, the Dice coefficients were 91.8, 82.5 and 86.8% for WT, ET and TC, respectively. Compared with other state-of-the-art methods, SDS-Net achieved superior segmentation performance with fewer parameters and less computational cost, under the condition of 2.52 M counts and 68.18 G FLOPs.

Understandings, Attitudes, and Barriers About Diabetes Care: Analysis of Factors Influencing Community Pharmacists in China.

PURPOSE: Diabetes mellitus (DM) has been known as a major chronic health problem in China. Suboptimal management of diabetic patients may incur serious complications, even death. The quality of post-hospital care has a good relationship with community pharmacists. However, data describing the current situation from care between community pharmacists and patients in China are lacking. Our article is to investigate community pharmacists' activities, evaluate their attitudes towards providing diabetes care, assess their understandings, and identify perceived barriers. METHODS: A survey divided into four parts was carried out randomly in China. The part of basic characteristics, understandings, and pharmacists' perceived barriers was rated with a few listed choices scales, while the Likert scale was used to identify on the part of attitudes. Quantitative data were shown in frequency and valid percent. One-way analysis of variance (ANOVA) and non-parametric test conducted on data. A P-value ≤0.05 was considered statistically significant. RESULTS: A total of 737 surveys were collected. The respondent pharmacists maintained a simply moderate understanding of diabetes care and the pharmaceutical services provided met basic needs rather than clinical ones, though they showed a good momentum towards providing better service. The respondent pharmacists considered patients lacking knowledge on self-management, shortage of funds as the main barriers. CONCLUSION: Efforts are supposed to make to expand pharmacists' scope of practice, lessen patients' reluctance, and create platforms for pharmacists receiving further education.