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import keras.backend as K
# dice loss as defined above for 4 classes
def dice_coef(y_true, y_pred, smooth=1.0):
class_num = 4
for i in range(class_num):
y_true_f = K.flatten(y_true[:,:,:,i])
y_pred_f = K.flatten(y_pred[:,:,:,i])
intersection = K.sum(y_true_f * y_pred_f)
loss = ((2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))
# K.print_tensor(loss, message='loss value for class {} : '.format(SEGMENT_CLASSES[i]))
if i == 0:
total_loss = loss
else:
total_loss = total_loss + loss
total_loss = total_loss / class_num
# K.print_tensor(total_loss, message=' total dice coef: ')
return total_loss
# define per class evaluation of dice coef
# inspired by https://github.com/keras-team/keras/issues/9395
def dice_coef_necrotic(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,1] * y_pred[:,:,:,1]))
return (2. * intersection) / (K.sum(K.square(y_true[:,:,:,1])) + K.sum(K.square(y_pred[:,:,:,1])) + epsilon)
def dice_coef_edema(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,2] * y_pred[:,:,:,2]))
return (2. * intersection) / (K.sum(K.square(y_true[:,:,:,2])) + K.sum(K.square(y_pred[:,:,:,2])) + epsilon)
def dice_coef_enhancing(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,3] * y_pred[:,:,:,3]))
return (2. * intersection) / (K.sum(K.square(y_true[:,:,:,3])) + K.sum(K.square(y_pred[:,:,:,3])) + epsilon)
# Computing Precision
def precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
# Computing Sensitivity
def sensitivity(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
return true_positives / (possible_positives + K.epsilon())
# Computing Specificity
def specificity(y_true, y_pred):
true_negatives = K.sum(K.round(K.clip((1-y_true) * (1-y_pred), 0, 1)))
possible_negatives = K.sum(K.round(K.clip(1-y_true, 0, 1)))
return true_negatives / (possible_negatives + K.epsilon()) |