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from tensorflow.keras.losses import binary_crossentropy | ||
import tensorflow.keras.backend as K | ||
import tensorflow as tf | ||
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epsilon = 1e-5 | ||
smooth = 1 | ||
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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 | ||
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def dice_loss(y_true, y_pred): | ||
loss = 1 - dsc(y_true, y_pred) | ||
return loss | ||
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def bce_dice_loss(y_true, y_pred): | ||
loss = binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred) | ||
return loss | ||
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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 | ||
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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 | ||
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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 | ||
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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) | ||
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def tversky_loss(y_true, y_pred): | ||
return 1 - tversky(y_true,y_pred) | ||
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def focal_tversky(y_true,y_pred): | ||
pt_1 = tversky(y_true, y_pred) | ||
gamma = 0.75 | ||
return K.pow((1-pt_1), gamma) |