除了我们熟知的miou指标外,Dice,F1-score这2个指标也是分割问题中常用的指标。
P(Precision) = TP/(TP + FP);
R(Recall) = TP/(TP + FN);
IoU = TP/(TP + FP + FN)
DICE (dice coefficient) = 2*TP/(FP + FN + 2 * TP)=2*IoU/(IoU+1)
F1-score = (2*P*R)/(P + R)=2*TP/(FP + FN + 2 * TP)=DICE
按照公式来看,其实Dice==F1-score
但是我看论文里面虽然提供的公式是我上面贴的公式,但是他们的两个数值完全不一样,甚至还相差较大。
比如:这篇论文提供了权重和代码,我测出来的两个数值也是一样的,而且代码里面的计算公式和上面贴的公式一样,但是论文中给出来的结果就不一样了。
还有这篇,这篇没有权重但是论文里写了公式
那么这个是怎么造成的呢?
其实这里的dice准确的说叫soft dice,出自论文V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation,
Fbeta_score=(1 + beta2)*TP/[ beta2 *(TP+FN)+(TP+FP)] = (1 + beta2)* (true * pred) / [beta2 *true + pred]
F1_score=2*TP/[(TP+FN)+(TP+FP)] = 2* (true * pred) / [true + pred]
Dice = 2* (true * pred_score) / [true*true + pred_score* pred_score]
两个公式的区别,就是Dice使用的不是预测的结果pred,而是预测的结果的得分pred_score,并且做了平方操作。
这样最终算下来,就会得到Dice>F1_score
def f_beta(y_true, y_pred, reduce_axes, beta=1., epsilon=1e-6):
"""
Differentiable F-beta score
F_beta = (1 + beta^2) * TP / [beta^2 * (TP + FN) + (TP + FP)]
= (1 + beta^2) (true * pred) / [beta^2 * true + pred]
Using this formula, we don't have to use precision and recall.
F1 score is an example of F-beta score with beta=1.
"""
beta2 = beta ** 2 # beta squared
numerator = (1 + beta2) * (y_true * y_pred).sum(reduce_axes)
denominator = beta2 * y_true.sum(reduce_axes) + y_pred.sum(reduce_axes)
denominator = denominator.clamp(min=epsilon)
return numerator / denominator
def dice(y_true, y_pred, reduce_axes, beta=1., epsilon=1e-6):
"""
Compute soft dice coefficient according to V-Net paper.
(https://arxiv.org/abs/1606.04797)
Unlike F-beta score, dice uses squared probabilities
instead of probabilities themselves for denominator.
Due to the squared entries, gradients will be y_true and y_pred instead of 1.
"""
beta2 = beta ** 2 # beta squared
numerator = (1 + beta2) * (y_true * y_pred).sum(reduce_axes)
denominator = beta2 * y_true.square().sum(reduce_axes) + y_pred.square().sum(reduce_axes)
denominator = denominator.clamp(min=epsilon)
return numerator / denominator