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IBBM / Cascaded-FCN

Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"
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question #19

Closed bxyan closed 7 years ago

bxyan commented 7 years ago

step2_weights.caffemodel I cannot find

PatrickChrist commented 7 years ago

Please look into the repo.

saqibqamar commented 6 years ago

why use class weight 1, 13,120. Can we use other number ?...On what basis you are using this weights..

class_loss_weights: 1 class_loss_weights: 13 class_loss_weights: 120

mohamed-ezz commented 6 years ago

The weight given to a certain class was inversely proportional to the frequency of that class in the dataset. In our case the weight was inversely proportional to the number of pixels for each class in the entire dataset. So that the resulting contribution is roughly equal for all classes.

This was inspired from the U-Net paper. In equation (2) in https://arxiv.org/pdf/1505.04597.pdf, we use w_c as the weight. We have not used the second term.