Assessment of glomerular morphological patterns by deep learning algorithms [Research Data] (doi:10.11588/data/JWZ2CK)

View:

Part 1: Document Description
Part 2: Study Description
Part 5: Other Study-Related Materials
Entire Codebook

Assessment of glomerular morphological patterns by deep learning algorithms [Research Data]

doi:10.11588/data/JWZ2CK

heiDATA

2022-02-07

1

Weis, Cleo-Aron, 2022, "Assessment of glomerular morphological patterns by deep learning algorithms [Research Data]", https://doi.org/10.11588/data/JWZ2CK, heiDATA, V1

Assessment of glomerular morphological patterns by deep learning algorithms [Research Data]

CNN-based glomerulus classification

doi:10.11588/data/JWZ2CK

Weis, Cleo-Aron (Institute of Pathology Mannheim,Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany)

heiDATA

Weis, Cleo-Aron

https://doi.org/10.11588/data/JWZ2CK

Medicine, Health and Life Sciences

Nephropathology

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.<br /> The models are: AlexNet [1], ResNet18-152 [2], ResNet34 [2], ResNet50 [2], ResNet101 [2], ResNet152 [2], vgg11 [3], vgg16 [3], vgg19 [3], squeeznet [4], inception [5], and densenet121 [6].<br /> The patterns are pattern 01: normal glomerulus, pattern 02: amyloidosis, pattern 03: nodular sclerosis, pattern 04: global sclerosis, pattern 05: mesangial expansion, pattern 06: membranoproliferative glomerulonephritis (MPGN), pattern 07: necrosis, pattern 08: segmental sclerosis, and pattern 09: other structures / default. <br /> References:<br /> <ol> <li>Krizhevsky, A., One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997, 2014.</li> <li>He, K., et al. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.</li> <li>Simonyan, K. and A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.</li> <li>Landola, F.N., et al., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360, 2016.</li> <li>Szegedy, C., et al. Rethinking the inception architecture for computer vision. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.</li> <li>Huang, G., et al. Densely connected convolutional networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.</li> </ol>

Weis, CA., Bindzus, J.N., Voigt, J. et al. Assessment of glomerular morphological patterns by deep learning algorithms. J Nephrol (2022).

https://doi.org/10.1007/s40620-021-01221-9

Weis, CA., Bindzus, J.N., Voigt, J. et al. Assessment of glomerular morphological patterns by deep learning algorithms. J Nephrol (2022).

Label:

model_alex.pt

Text:

application/octet-stream

Label:

model_densenet121.pt

Text:

application/octet-stream

Label:

model_inception.pt

Text:

application/octet-stream

Label:

model_ResNet101.pt

Text:

application/octet-stream

Label:

model_ResNet152.pt

Text:

application/octet-stream

Label:

model_ResNet18.pt

Text:

application/octet-stream

Label:

model_ResNet34.pt

Text:

application/octet-stream

Label:

model_ResNet50.pt

Text:

application/octet-stream

Label:

model_squeeznet.pt

Text:

application/octet-stream

Label:

model_vgg11.pt

Text:

application/octet-stream

Label:

model_vgg16.pt

Text:

application/octet-stream

Label:

model_vgg19.pt

Text:

application/octet-stream

Label:

test-set.zip

application/zip