srch

对于ce loss，当前的点的梯度仅和当前预测值与label的距离相关，预测越接近label，梯度越小。当网络预测接近0或1时，梯度依然保持该特性。 对比发现， 训练前中期，dice loss下正样本的梯度值相对于ce loss，颜色更亮，值更大。说明dice loss 对挖掘正样本更加有优势。

[docs]class DiceCELoss(_Loss): """ Compute both Dice loss and Cross Entropy Loss, and return the weighted sum of these two losses. The details of Dice loss is ...

12.08.2019 · CrossEntropy could take values bigger than 1. I am actually trying with Loss = CE - log (dice_score) where dice_score is dice coefficient (opposed as the dice_loss where basically dice_loss = 1 - dice_score. I will wait for the results but some hints or help would be really helpful. Megh_Bhalerao (Megh Bhalerao) August 25, 2019, 3:08pm #3.

Jan 04, 2018 · I would recommend you to use Dice loss when faced with class imbalanced datasets, which is common in the medicine domain, for example. Also, Dice loss was introduced in the paper "V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation" and in that work the authors state that Dice loss worked better than mutinomial logistic loss with sample re-weighting

Nov 19, 2018 · My dice and ce decrease, but then suddenly dice increases and CE jumps up a bit, this keeps happening to dice. I have been trying all day to fix this but can’t get my code to run. I am running on only 10 data points to overfit my data but it just is not happening. Any help would be greatly appreciated. Plots of dice(top) and CE: Loss curve

30.11.2018 · 2、Dice Loss. 首先定义两个轮廓区域的相似程度，用A、B表示两个轮廓区域所包含的点集，定义为： 那么loss为： 可以看出，Dice Loss其实也可以分为两个部分，一个是前景的loss，一个是物体的loss，但是在实现中，我们往往只关心物体的loss，Keras的实现如下：

Since we are dealing with individual pixels, I can understand why one would use CE loss. But Dice loss is not clicking. Quote. Follow. Bookmark ...

Hi, I am conducting a segmentation task with only one target structure. Now I try to modify the loss function as Dice + CE loss, ...

3、Dice Loss VS CE. 语义分割中一般用交叉熵来做损失函数，而评价的时候却使用IOU来作为评价指标，（GIOU这篇文章中说道：给定优化指标本身与代理损失函数之间的选择，最优选择就是指标本身。）为什么不直接拿类似IOU的损失函数来进行优化呢？

08.09.2020 · 1、Dice loss. dice loss 的提出是在U-net中，其中的一段原因描述是在感兴趣的解剖结构仅占据扫描的非常小的区域，从而使学习过程陷入损失函数的局部最小值。. 所以要加大前景区域的权重。. Dice 可以理解为是两个轮廓区域的相似程度，用A、B表示两个轮廓区域所 ...

Vanilla CE loss is assigned proportional to the instance/class area. DICE loss is assigned to instance/class without respect to area. Adding ...

4.3 Evaluation To we evaluate the performance of KD loss we compare the distilled model with models trained on baseline Cross Entropy (CE) loss, Dice loss ...

3、Dice Loss VS CE. 语义分割中一般用交叉熵来做损失函数，而评价的时候却使用IOU来作为评价指标，（GIOU这篇文章中说道：给定优化指标本身与代理损失函数之间的选择，最优选择就是指标本身。）为什么不直接拿类似IOU的损失函数来进行优化呢？

So is there like some kind of normalization that should be performed on the CE loss to bring it in the same scale to that of the dice loss?

22.08.2020 · 对于ce loss，当前的点的梯度仅和当前预测值与label的距离相关，预测越接近label，梯度越小。当网络预测接近0或1时，梯度依然保持该特性。 对比发现， 训练前中期，dice loss下正样本的梯度值相对于ce loss，颜色更亮，值更大。说明dice loss 对挖掘正样本更加有优势。

It was found that when using Dice loss, SS loss or CE+Dice loss for the curriculum learning necessitates only a single stage of curriculum learning (no ...

LiverVesselSegmentationNetwork / loss_dice_and_ce.py / Jump to Code definitions softmax_helper Function sum_tensor Function get_tp_fp_fn Function CrossentropyND Class forward Function SoftDiceLoss Class __init__ Function forward Function DC_and_CE_loss Class __init__ Function forward Function

Aug 12, 2019 · CrossEntropy could take values bigger than 1. I am actually trying with Loss = CE - log (dice_score) where dice_score is dice coefficient (opposed as the dice_loss where basically dice_loss = 1 - dice_score. I will wait for the results but some hints or help would be really helpful. Megh_Bhalerao (Megh Bhalerao) August 25, 2019, 3:08pm #3.

27.07.2019 · Dice 定义为2倍交集/和， 范围在[0，1]： Dice Loss 取反或者用1-，定义为： 2、Dice Loss 与 BCE 的结合各自的作用。 Dice Loss 与交叉熵经常搭配使用，具有以下优点： 1） Dice Loss 相当于从全局上进行考察，B CE 是从微观上逐像素进行拉近，角度互补。

What is the intuition behind using Dice loss instead of Cross-Entroy loss for Image/Instance segmentation problems? Since we are dealing with individual pixels, I can understand why one would use CE loss. But Dice loss is not clicking.

One compelling reason for using cross-entropy over dice-coefficient or the similar IoU metric is that the gradients are nicer.