losses.py

from keras.losses import binary_crossentropy
import keras.backend as K
import tensorflow as tf 

epsilon = 1e-5
smooth = 1

def dsc(y_true, y_pred):
    smooth = 1.
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    score = (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
    return score

def dice_loss(y_true, y_pred):
    loss = 1 - dsc(y_true, y_pred)
    return loss

def bce_dice_loss(y_true, y_pred):
    loss = binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)
    return loss

def confusion(y_true, y_pred):
    smooth=1
    y_pred_pos = K.clip(y_pred, 0, 1)
    y_pred_neg = 1 - y_pred_pos
    y_pos = K.clip(y_true, 0, 1)
    y_neg = 1 - y_pos
    tp = K.sum(y_pos * y_pred_pos)
    fp = K.sum(y_neg * y_pred_pos)
    fn = K.sum(y_pos * y_pred_neg) 
    prec = (tp + smooth)/(tp+fp+smooth)
    recall = (tp+smooth)/(tp+fn+smooth)
    return prec, recall

def tp(y_true, y_pred):
    smooth = 1
    y_pred_pos = K.round(K.clip(y_pred, 0, 1))
    y_pos = K.round(K.clip(y_true, 0, 1))
    tp = (K.sum(y_pos * y_pred_pos) + smooth)/ (K.sum(y_pos) + smooth) 
    return tp 

def tn(y_true, y_pred):
    smooth = 1
    y_pred_pos = K.round(K.clip(y_pred, 0, 1))
    y_pred_neg = 1 - y_pred_pos
    y_pos = K.round(K.clip(y_true, 0, 1))
    y_neg = 1 - y_pos 
    tn = (K.sum(y_neg * y_pred_neg) + smooth) / (K.sum(y_neg) + smooth )
    return tn 

def tversky(y_true, y_pred):
    y_true_pos = K.flatten(y_true)
    y_pred_pos = K.flatten(y_pred)
    true_pos = K.sum(y_true_pos * y_pred_pos)
    false_neg = K.sum(y_true_pos * (1-y_pred_pos))
    false_pos = K.sum((1-y_true_pos)*y_pred_pos)
    alpha = 0.7
    return (true_pos + smooth)/(true_pos + alpha*false_neg + (1-alpha)*false_pos + smooth)

def tversky_loss(y_true, y_pred):
    return 1 - tversky(y_true,y_pred)

def focal_tversky(y_true,y_pred):
    pt_1 = tversky(y_true, y_pred)
    gamma = 0.75
    return K.pow((1-pt_1), gamma)