41 to 50 of 71 Results
Jul 27, 2021 -
Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]
ZIP Archive - 3.7 GB -
MD5: fb27263c6f7b96b4cd118bff95229be2
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Jul 27, 2021 -
Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]
ZIP Archive - 2.8 GB -
MD5: 711fc1b987b5a0d8312f5727a2edde15
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Jul 27, 2021 -
Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]
ZIP Archive - 3.8 GB -
MD5: ca25fcb98acaccccca6e3864f78359c6
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Jul 27, 2021 -
Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]
ZIP Archive - 3.5 GB -
MD5: 4486f2d7c5e908cf0bec2ba857e059bf
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Jul 27, 2021 -
Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]
ZIP Archive - 484.6 MB -
MD5: 5ca5dcacd937be80d606597dcc878f7e
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Feb 7, 2022
Weis, Cleo-Aron, 2022, "Assessment of glomerular morphological patterns by deep learning algorithms [Research Data]", https://doi.org/10.11588/data/JWZ2CK, heiDATA, V1
Test data and models to the paper "Assessment of glomerular morphological patterns by deep learning algorithms". Different, from other groups, defined CNN-models (saved as .pt-files) are trained to identify nine predefined patterns of glomerular changes. The models are: AlexNet [... |
Feb 7, 2022 -
Assessment of glomerular morphological patterns by deep learning algorithms [Research Data]
Unknown - 221.5 MB -
MD5: 9dc706ae346e67dc1bafd73159279e6c
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Feb 7, 2022 -
Assessment of glomerular morphological patterns by deep learning algorithms [Research Data]
Unknown - 27.1 MB -
MD5: 784a68762bf7f8cf8d68e68dd34178c6
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Feb 7, 2022 -
Assessment of glomerular morphological patterns by deep learning algorithms [Research Data]
Unknown - 96.2 MB -
MD5: 84f74583b9067e6641f698be0610cf81
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Feb 7, 2022 -
Assessment of glomerular morphological patterns by deep learning algorithms [Research Data]
Unknown - 162.8 MB -
MD5: 282550f75a2e09a0714cb537fdb7cd00
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