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Figure 1—figure supplement 1.

ID: ZDB-IMAGE-201209-33
Publication: Segebarth et al., 2020 - On the objectivity, reliability, and validity of deep learning enabled bioimage analyses
All Figures: Figures for Segebarth et al., 2020

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Figure 1—figure supplement 1. U-Net architecture.

The U-Net architecture was adapted from Falk et al., 2019. The input and output shapes denote (minibatch size x height x width x channels), where the mini-batch size is not specified. If applicable, the names of the layers describe the containing operations (e.g.: Conv2D_BN_leakyReLU represents a 2D convolution followed by a batch normalization layer and a leakyReLu activation function). All convolutional layers were instantiated with a kernel size of 3 × 3, a stride of one and, no padding, except for the last convolution (1 × 1 kernel). The leaky ReLU has a leakage factor of 0.1 and the max-pooling operation a stride of two. The up-convolution (Conv2DTranspose) has a kernel size of 2 × 2 and strides of two.

Acknowledgments

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