Update layer README
@@ -132,6 +132,7 @@ Dice Loss (no square) | Similar to Dice Loss, but probabilities are not squared
Generalised Dice Loss | | "Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations", Sudre, C. et. al. DLMIA 2017.| type_weight: default 'Square'. Indicates how the volume of each label is weighted. Square - Multiplication by the inverse of square of the volume / Simple - Multiplication by 1/V / Uniform - No weighting
Generalised Wasserstein Dice Loss | | "Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks", Fidon, L. et. al. MICCAI 2017 (BrainLes).
Sensitivity-Specificity Loss | | "Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation", Brosch et al, MICCAI 2015. | r: default 0.05. The 'sensitivity ratio' (authors suggest values from 0.01-0.10 will have similar effects)
+Tversky index | | "Tversky loss function for image segmentation using 3D fully convolutional deep networks", Sadegh S. et al., 2017 | `alpha` and `beta` are parameters that control the trade-off between false positives and false negatives
File: loss_regression.py
