metrics.py 文件源码

def dice_core_mask(mask):
    def dice_core_closure(y_true, y_pred):
        """
        Computes the Sorensen-Dice metric, where P come from class 1,2,3,4,5
                        TP
            Dice = 2 -------
                      T + P
        Parameters
        ----------
        y_true : keras.placeholder
            Placeholder that contains the ground truth labels of the classes
        y_pred : keras.placeholder
            Placeholder that contains the class prediction

        Returns
        -------
        scalar
            Dice metric
        """

        y_pred_decision = K.cast(y_pred / K.max(y_pred, axis=1, keepdims=True), 'int8')
        mask_true = K.sum(y_true[:, [1, 3, 4], :, :, :], axis=1)
        mask_pred = K.sum(y_pred_decision[:, [1, 3, 4], :, :, :], axis=1)

        y_sum = K.sum(mask * mask_true * mask_pred)

        return (2. * y_sum + K.epsilon()) / (K.sum(mask * mask_true) + K.sum(mask * mask_pred) + K.epsilon())

    return dice_core_closure