Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Hongfeng You"'
Publikováno v:
Complex & Intelligent Systems, Vol 9, Iss 5, Pp 5965-5974 (2023)
Abstract Deep convolutional neural network (CNN) has made great progress in medical image classification. However, it is difficult to establish effective spatial associations, and always extracts similar low-level features, resulting in redundancy of
Externí odkaz:
https://doaj.org/article/8a3c3124e03341bbad0dfa935705bda3
Publikováno v:
Complex & Intelligent Systems. 8:611-623
To obtain more semantic information with small samples for medical image segmentation, this paper proposes a simple and efficient dual-rotation network (DR-Net) that strengthens the quality of both local and global feature maps. The key steps of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3fbf046ef59876fb953e75d16195749d
https://doi.org/10.1007/s40747-021-00525-4
https://doi.org/10.1007/s40747-021-00525-4
Publikováno v:
Multimedia Tools and Applications. 82:5587-5603
Segmentation tasks in medical images have always been a hot topic in the medical imaging field. Compared with traditional images, medical images have richer semantics, which increases the difficulty of feature learning. This paper proposes a new end-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8bbdb79f22fb69620cbd90952b5cca69
https://doi.org/10.1007/s11042-021-11326-9
https://doi.org/10.1007/s11042-021-11326-9
Publikováno v:
Automatic Control and Computer Sciences. 54:560-571
In order to solve the problems of low gray scale contrast and blurred organ boundaries in some medical images, we proposed a joint algorithm of Multi-Attention Parallel CNNs and Independent Recurrent Neural Networks (MACIR) with word embedding techni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4c3a5f0b1198285333d491ca4c22dcfb
https://doi.org/10.3103/s0146411620060024
https://doi.org/10.3103/s0146411620060024
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 58:1281-1293
In the traditional remote sensing image recognition, the traditional features (e.g., color features and texture features) cannot fully describe complex images, and the relationships between image pixels cannot be captured well. Using a single model o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e7143fed0f1ff6048d192469daf14c92
https://doi.org/10.1109/tgrs.2019.2945591
https://doi.org/10.1109/tgrs.2019.2945591
Publikováno v:
Multimedia Tools and Applications. 82:5605-5605
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8aae45f9da277d325b94ca9239369221
https://doi.org/10.1007/s11042-022-14029-x
https://doi.org/10.1007/s11042-022-14029-x
Publikováno v:
Interdisciplinary sciences, computational life sciences. 14(1)
The diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-cancer peptides, this paper proposes a more suitable feature grouping sequence and spatial dimension-int
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4105249d78900eb0451e1685b7e608b0
https://pubmed.ncbi.nlm.nih.gov/34637113
https://pubmed.ncbi.nlm.nih.gov/34637113
Publikováno v:
Knowledge-Based Systems. 231:107456
To better retain the deep features of an image and solve the sparsity problem of the end-to-end segmentation model, we propose a new deep convolutional network model for medical image pixel segmentation, called MC-Net. The core of this network model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1084bb71a52c62699f0a90c05292a579
https://doi.org/10.1016/j.knosys.2021.107456
https://doi.org/10.1016/j.knosys.2021.107456