Type 1 gastric neuroendocrine tumors （gNETs） are usually small lesions, restricted to mucosal and sub-mucosal layers of corpus and fundus, with low aggressive behavior, for the majority of cases. Nevertheless, some ...Type 1 gastric neuroendocrine tumors （gNETs） are usually small lesions, restricted to mucosal and sub-mucosal layers of corpus and fundus, with low aggressive behavior, for the majority of cases. Nevertheless, some cases present aggressive behavior. The increasing incidence of gNETs brings together a new relevant problem： how to identify potentially aggressive type I gNETs. The challenging problem seems to be finding out signs or features able to predict potentially aggressive cases, allowing a tailored approach, since the involved societies dedicated to provide guidelines for management of these neoplasms apparently failed in producing staging systems able to accurately predict prognosis of these tumors. Additionally, it is also important to try to find out explanations for increasing incidence, as well as to identify potential targets aiming to reach better control of this neoplasia. Here, we discuss potential pathways implicated in aggressive behavior, as well as new strategies to improve clinical management of these tumors.展开更多
Retinal vessel segmentation is a significant problem in the analysis of fundus images.A novel deep learning structure called the Gaussian net(GNET)model combined with a saliency model is proposed for retinal vessel se...Retinal vessel segmentation is a significant problem in the analysis of fundus images.A novel deep learning structure called the Gaussian net(GNET)model combined with a saliency model is proposed for retinal vessel segmentation.A saliency image is used as the input of the GNET model replacing the original image.The GNET model adopts a bilaterally symmetrical structure.In the left structure,the first layer is upsampling and the other layers are max-pooling.In the right structure,the final layer is max-pooling and the other layers are upsampling.The proposed approach is evaluated using the DRIVE database.Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models.The proposed algorithm performs well in extracting vessel networks,and is more accurate than other deep learning methods.Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.展开更多
文摘Type 1 gastric neuroendocrine tumors （gNETs） are usually small lesions, restricted to mucosal and sub-mucosal layers of corpus and fundus, with low aggressive behavior, for the majority of cases. Nevertheless, some cases present aggressive behavior. The increasing incidence of gNETs brings together a new relevant problem： how to identify potentially aggressive type I gNETs. The challenging problem seems to be finding out signs or features able to predict potentially aggressive cases, allowing a tailored approach, since the involved societies dedicated to provide guidelines for management of these neoplasms apparently failed in producing staging systems able to accurately predict prognosis of these tumors. Additionally, it is also important to try to find out explanations for increasing incidence, as well as to identify potential targets aiming to reach better control of this neoplasia. Here, we discuss potential pathways implicated in aggressive behavior, as well as new strategies to improve clinical management of these tumors.
基金Project supported by the Natural Science Foundation of Fujian Province,China(No.2016J0129)the Educational Commission of Fujian Province of China(No.JAT170180)
文摘Retinal vessel segmentation is a significant problem in the analysis of fundus images.A novel deep learning structure called the Gaussian net(GNET)model combined with a saliency model is proposed for retinal vessel segmentation.A saliency image is used as the input of the GNET model replacing the original image.The GNET model adopts a bilaterally symmetrical structure.In the left structure,the first layer is upsampling and the other layers are max-pooling.In the right structure,the final layer is max-pooling and the other layers are upsampling.The proposed approach is evaluated using the DRIVE database.Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models.The proposed algorithm performs well in extracting vessel networks,and is more accurate than other deep learning methods.Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.
