Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Zengqiang Yan"'
Publikováno v:
IEEE Access, Vol 12, Pp 104291-104299 (2024)
Power equipment image segmentation is challenging as it involves objects of various scales/sizes, illustration conditions, and imaging angles, making task-specific deep learning approaches struggle to extract scene-invariant features for segmentation
Externí odkaz:
https://doaj.org/article/8f4be87609084085a37e1cbcf83360a6
Publikováno v:
Acta Cirúrgica Brasileira, Vol 38 (2023)
ABSTRACT Purpose: To investigate putative mechanism of wound healing for chitosan-based bisacurone gel against secondary burn wounds in rats. Methods: A second-degree burn wound with an open flame using mixed fuel (2 mL, 20 seconds) was induced in Sp
Externí odkaz:
https://doaj.org/article/1229ce89d870433889d5e6a4d51da9f9
Autor:
Tengfei Wang, Kaifeng Guan, Qiuju Su, Xiaotong Wang, Zengqiang Yan, Kailin Kuang, Yuan Wang, Qingde Zhang, Xiang Zhou, Bang Liu
Publikováno v:
Animals, Vol 12, Iss 12, p 1504 (2022)
Porcine Reproductive and Respiratory Syndrome (PRRS) is one of the serious infectious diseases that threatens the swine industry. Increasing evidence shows that gut microbiota plays an important role in regulating host immune responses to PRRS virus
Externí odkaz:
https://doaj.org/article/74499d6cfa664c30b766554a76db6d6f
Publikováno v:
IEEE Transactions on Circuits and Systems for Video Technology. 33:1762-1775
Publikováno v:
IEEE Journal of Biomedical and Health Informatics. :1-12
Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high pixel-wise accuracy, complete topology structure and robustness to various co
Publikováno v:
IEEE Journal of Biomedical and Health Informatics. 26:5165-5176
Cerebral ventricles are one of the prominent structures in the brain, segmenting which can provide rich information for brain-related disease diagnosis. Unfortunately, cerebral ventricle segmentation in complex clinical cases, such as in the coexiste
Publikováno v:
European Food Research and Technology. 248:1553-1561
Publikováno v:
IEEE journal of biomedical and health informatics. 26(11)
The performance of deep networks for medical image analysis is often constrained by limited medical data, which is privacy-sensitive. Federated learning (FL) alleviates the constraint by allowing different institutions to collaboratively train a fede
Publikováno v:
IEEE Journal of Biomedical and Health Informatics. 25:2615-2628
Privacy concerns make it infeasible to construct a large medical image dataset by fusing small ones from different sources/institutions. Therefore, federated learning (FL) becomes a promising technique to learn from multi-source decentralized data wi
Objective: Transformers, born to remedy the inadequate receptive fields of CNNs, have drawn explosive attention recently. However, the daunting computational complexity of global representation learning, together with rigid window partitioning, hinde
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d21944e990a2a118c5966586f2279b82
http://arxiv.org/abs/2206.14409
http://arxiv.org/abs/2206.14409