Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Guanglu Ye"'
Publikováno v:
PLoS ONE, Vol 16, Iss 5, p e0251521 (2021)
Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image ana
Externí odkaz:
https://doaj.org/article/7b5765269af4427d823e95c053205a51
Publikováno v:
ISCID (1)
Recent work has shown that the network for natural language processing (NLP) task can be deeper as well as more accurate by applying attention mechanism with residual. However, the original features of training data will be lost after multiple operat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b4058fd5c5b0acb438f26d9f764fa81
https://doi.org/10.1109/iscid.2019.00057
https://doi.org/10.1109/iscid.2019.00057
Autor:
Simin He, Chenchen Wu, Junqiu Yue, Jingfan Zhou, Jun Ruan, Yanggeling Zhang, Jianlian Wang, Zhikui Zhu, Guanglu Ye
Publikováno v:
ICACI
Deep learning is widely used in medical applications in view of the excellent performance it achieved in image processing. Early methods of diagnosis on whole slide images (WSIs) is usually based on dense sampling which is time-consuming and requires
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d95d3b5d79c2452c8da3a16deef5d7c1
https://doi.org/10.1109/icaci.2019.8778616
https://doi.org/10.1109/icaci.2019.8778616
Publikováno v:
2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN).
In order to identify the breast cancer region, it is necessary to discriminate the pathological image of breast cancer pixel by pixel. This is a very huge work for machine learning. Therefore, the preprocessing of superpixel segmentation of breast ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9cdc7876da6bfecf5e688811e1bde09c
https://doi.org/10.1109/iccsn.2019.8905358
https://doi.org/10.1109/iccsn.2019.8905358
Autor:
Chenchen Wu, Yanggeling Zhang, Junqiu Yue, Jianlian Wang, Zhikui Zhu, Jun Ruan, Guanglu Ye, Jingfan Zhou, Simin He
Publikováno v:
ICACI
Breast cancer has always been the main killer of women. The constantly development of Convolutional Neural Network (CNN) greatly improved the possibility of early diagnostics of breast cancer owing to its high efficiency and accuracy. In this paper,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e7b2264132ab05a0ba341369778e4ad1
https://doi.org/10.1109/icaci.2019.8778592
https://doi.org/10.1109/icaci.2019.8778592
Autor:
Jun Ruan, Jianlian Wang, Chenchen Wu, Guanglu Ye, Yanggeling Zhang, Simin He, Junqiu Yue, Yi Long, Jingfan Zhou
Publikováno v:
ISCID (1)
The automatic classification of pathological images of breast cancer has important clinical value. In order to improve the accuracy and efficiency of cancer detection, we implement two classifications in this paper. (1) Train deep convolutional neura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4bf890e748f758e559c9c73bb9811cdd
https://doi.org/10.1109/iscid.2018.00007
https://doi.org/10.1109/iscid.2018.00007
Autor:
Jingfan Zhou, Jianlian Wang, Guanglu Ye, Chenchen Wu, Junqiu Yue, Simin He, Yanggeling Zhang, Jun Ruan
Publikováno v:
ISCID (1)
For identifying Human epidermal growth factor receptor 2(HER2) Scores and magnification in different histopathology whole slide images(WSIs) of Breast cancer(BCa), an efficient multi-task convolutional Neural Network(CNN) is proposed. To prove its ef
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e338fb09383bd8d134a182a6635b1d2
https://doi.org/10.1109/iscid.2018.00008
https://doi.org/10.1109/iscid.2018.00008
Publikováno v:
PLoS ONE
PLoS ONE, Vol 16, Iss 5, p e0251521 (2021)
PLoS ONE, Vol 16, Iss 5, p e0251521 (2021)
Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image ana
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4593bcc25fde05fff18af30fc7e6ef4e
https://doi.org/10.1371/journal.pone.0251521
https://doi.org/10.1371/journal.pone.0251521