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

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>