RESUMO
Semantic segmentation and instance segmentation based on deep learning play a significant role in intelligent dairy goat farming. However, these algorithms require a large amount of pixel-level dairy goat image annotations for model training. At present, users mainly use Labelme for pixel-level annotation of images, which makes it quite inefficient and time-consuming to obtain a high-quality annotation result. To reduce the annotation workload of dairy goat images, we propose a novel interactive segmentation model called UA-MHFF-DeepLabv3+, which employs layer-by-layer multi-head feature fusion (MHFF) and upsampling attention (UA) to improve the segmentation accuracy of the DeepLabv3+ on object boundaries and small objects. Experimental results show that our proposed model achieved state-of-the-art segmentation accuracy on the validation set of DGImgs compared with four previous state-of-the-art interactive segmentation models, and obtained 1.87 and 4.11 on mNoC@85 and mNoC@90, which are significantly lower than the best performance of the previous models of 3 and 5. Furthermore, to promote the implementation of our proposed algorithm, we design and develop a dairy goat image-annotation system named DGAnnotation for pixel-level annotation of dairy goat images. After the test, we found that it just takes 7.12 s to annotate a dairy goat instance with our developed DGAnnotation, which is five times faster than Labelme.
RESUMO
Introduction: The use of Wendan decoction (WDD) as a therapy for nonalcoholic fatty liver disease (NAFLD) has been studied in many clinical trials, and some of them showed that WDD is effective for treating this condition. However, no comprehensive research to evaluate the clinical efficacy of WDD in NAFLD patients had been performed. This systematic review and meta-analysis sought to provide an in-depth inquiry into the data currently available about the safety and effectiveness of WDD to treat NAFLD. Methods: We examined the primary database for any reports of randomized controlled trials (RCTs) including WDD and its effectiveness in treating NAFLD. We used the Jadad rating scale to determine the overall quality of the selected RCTs, and we searched the Cochrane Reviewer's Handbook for criteria for potential bias. The primary findings from the included RCTs were recorded, and the meta-analysis was performed using RevMan5.4 software developed by the Cochrane Collaboration. Results: We retrieved ten RCTs that were suitable for this evaluation and included them in a systematic review and meta-analysis. The quality and risk of bias in the included RCTs were assessed. The meta-analysis showed that the total clinical effective rate was substantially greater in the WDD cohort compared with that in the control cohort, and liver function, blood lipid indices, and blood glucose-related indicators were substantially improved in the WDD-treated cohort compared with those in the control cohort. There was no significant difference in the incidence of adverse events between the two cohorts. Conclusion: WDD is safe and effective for treating NAFLD, which is advantageous for the patients' liver function as well as their blood lipid indices and blood glucose-related indicators.
