(conv => BN => ReLU) * 2

Implementations based on the Unet3D paper: https://arxiv.org/pdf/1706.00120.pdf

to keep the spatial dims o=i, this formula is applied o = [i + 2*p - k - (k-1)*(d-1)]/s + 1

For transpose conv o = output, p = padding, k = kernel_size, s = stride, d = dilation o = (i -1)*s - 2*p + k + output_padding = (i-1)*2 +

Dice loss of binary class Args: smooth: A float number to smooth loss, and avoid NaN error, default: 1 p: Denominator value: \sum{

Implementation based on https://arxiv.org/abs/1708.00573 Trainable params: 1,783,408 (roughly 1.8 mentioned in the paper)

Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>` Based on the implementation of https://github.com/tbuikr/3D-Skip

Implementations based on the Vnet paper: https://arxiv.org/abs/1606.04797