胃肠道惰性T细胞淋巴瘤(indolent T-cell lymphoma of the gastrointestinal tract,ITCL-GI)是一种罕见的低级别、单克隆性、非嗜上皮性T细胞淋巴组织增生性疾病。本文报道1例73岁男性,小肠ITCL-GI同时发生乙状结肠弥漫大B细胞淋巴瘤的...胃肠道惰性T细胞淋巴瘤(indolent T-cell lymphoma of the gastrointestinal tract,ITCL-GI)是一种罕见的低级别、单克隆性、非嗜上皮性T细胞淋巴组织增生性疾病。本文报道1例73岁男性,小肠ITCL-GI同时发生乙状结肠弥漫大B细胞淋巴瘤的病例,小肠病变经T淋巴细胞受体基因检测显示单克隆性重排。ITCL-GI应与溃疡性结肠炎及肠病相关性T细胞淋巴瘤相鉴别。展开更多
Retinal images play an essential role in the early diagnosis of ophthalmic diseases.Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the reti...Retinal images play an essential role in the early diagnosis of ophthalmic diseases.Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background.At the same time,automated models struggle to capture representative and discriminative retinal vascular features.To fully utilize the structural information of the retinal blood vessels,we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network(PCRTAM-Net).PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the network.In addition,the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow.A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature dependencies.We evaluate the proposed PCRTAM-Net on four publicly available datasets,DRIVE,CHASE_DB1,STARE,and HRF.Our model achieves state-of-the-art performance of 97.10%,97.70%,97.68%,and 97.14%for ACC and 83.05%,82.26%,84.64%,and 81.16%for F1,respectively.展开更多
文摘胃肠道惰性T细胞淋巴瘤(indolent T-cell lymphoma of the gastrointestinal tract,ITCL-GI)是一种罕见的低级别、单克隆性、非嗜上皮性T细胞淋巴组织增生性疾病。本文报道1例73岁男性,小肠ITCL-GI同时发生乙状结肠弥漫大B细胞淋巴瘤的病例,小肠病变经T淋巴细胞受体基因检测显示单克隆性重排。ITCL-GI应与溃疡性结肠炎及肠病相关性T细胞淋巴瘤相鉴别。
基金supported by the Open Funds from Guangxi Key Laboratory of Image and Graphic Intelligent Processing under Grant No.GIIP2209the National Natural Science Foundation of China under Grant Nos.62172120 and 62002082the Natural Science Foundation of Guangxi Province of China under Grant Nos.2019GXNSFAA245014 and 2020GXNSFBA238014.
文摘Retinal images play an essential role in the early diagnosis of ophthalmic diseases.Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background.At the same time,automated models struggle to capture representative and discriminative retinal vascular features.To fully utilize the structural information of the retinal blood vessels,we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network(PCRTAM-Net).PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the network.In addition,the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow.A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature dependencies.We evaluate the proposed PCRTAM-Net on four publicly available datasets,DRIVE,CHASE_DB1,STARE,and HRF.Our model achieves state-of-the-art performance of 97.10%,97.70%,97.68%,and 97.14%for ACC and 83.05%,82.26%,84.64%,and 81.16%for F1,respectively.
