The automatic segmentation of the skin lesion on dermoscopy images is an important step for diagnosing the melanoma. However, the skin lesion segmentation is still a challenging task due to the blur lesion border, low contrast between the skin cancer region and normal tissue background, and various sizes of cancer regions. In this paper, we propose a deep supervised multi-scale network (DSM-Network), which achieves satisfied skin cancer segmentation result by utilizing the side-output layers of the network to aggregate information from shallow&deep layers, and designing a multi-scale connection block to handle a variety of cancer sizes' changes. Moreover, a post-processing of the contour refinement strategy is adopted by a conditional random field (CRF) model to further improve the segmentation results. Extensive experiments on two public datasets: ISBI 2017 and PH2 have demonstrated that our designed DSM-Network has gained competitive performance compared with other state-of-the-art methods.INDEX TERMS Skin cancer, dermoscopy image, deep supervised learning, multi-scale feature, conditional random field.
BACKGROUND: Gastric mucosal injury caused by ethanol is a common gastrointestinal disease. Quinoa (Chenopodium quinoa Willd.), as a nutrient-rich grain, plays a significant role in preventing and treating gastric mucosal damage. The present study aimed to explore the protective effect of quinoa on alcohol-induced gastric mucosal damage and its possible mechanism.RESULTS: The ethanol-induced gastric mucosal injury rat model was used for in vivo experiments and H 2 O 2 -induced GES-1 cells for in vitro experiments to elucidate the protective effect of quinoa. The results show that quinoa water extract can increase the superoxide dismutase level and decrease the malondialdehyde level in vitro and in vivo. Furthermore, quinoa also reduced the bleeding point and bleeding area in rats with ethanol-induced gastric mucosal injury and improved gastric histopathological changes. H 2 O 2 significantly increased the levels of inflammatory factors in GES-1 cells, which were markedly ameliorated by quinoa water extract. Likewise, quinoa water extract regulated the protein expression levels of Nrf2, Keap1, HO-1, p-IKK, and p-NF-κB through Nrf2 and nuclear factor-κB signaling pathways, reducing the production of oxidative stress and inflammation, thereby repairing the damaged gastric mucosa.
CONCLUSION:The findings of this study demonstrated that quinoa shows protective effect against ethanol-induced gastric mucosal injury through its anti-inflammatory and anti-oxidant effects. We propose that our research will provide a reference for quinoa as a functional food.
Lung cancer mortality is currently the highest among all kinds of fatal cancers. With the help of computer-aided detection systems, a timely detection of malignant pulmonary nodule at early stage could improve the patient survival rate efficiently. However, the sizes of the pulmonary nodules are usually various, and it is more difficult to detect small diameter nodules. The traditional convolution neural network uses pooling layers to reduce the resolution progressively, but it hampers the network’s ability to capture the tiny but vital features of the pulmonary nodules. To tackle this problem, we propose a novel 3D spatial pyramid dilated convolution network to classify the malignancy of the pulmonary nodules. Instead of using the pooling layers, we use 3D dilated convolution to learn the detailed characteristic information of the pulmonary nodules. Furthermore, we show that the fusion of multiple receptive fields from different dilated convolutions could further improve the classification performance of the model. Extensive experimental results demonstrate that our model achieves a better result with an accuracy of 88 . 6 % , which outperforms other state-of-the- art methods.
The spatial position of the seat base is complicated. The contour structure of arc groove is complex and its contour feature extraction is difficult. Aiming at the above problems, this paper presents an improved corner detection algorithm based on Curvature Scale Space (CSS), which uses adaptive local curvature and dynamic angle threshold to accurately extract corner points of arc groove contour. The correct corner positioning error of the algorithm proposed in this paper is only 0.99 pixel. The experimental results show that this method can effectively implement contour feature extraction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.