像素级调制Dice损失用于医学图像分割
Pixel-wise Modulated Dice Loss for Medical Image Segmentation
AI总结：
本研究提出像素级调制Dice损失，以极低计算成本同时处理医学分割中的类别不平衡和难度不平衡，在三个常用任务上优于现有方法。
摘要：
类别不平衡和难度不平衡是影响神经网络在医学分割任务中性能的两种数据不平衡类型。在类别不平衡中，损失由多数类主导；在难度不平衡中，损失由易分类像素主导。这导致训练效率低下。基于几何度量的Dice损失，与直接从分类任务中采用的交叉熵（CE）损失相比，在处理类别不平衡方面非常有效。为解决难度不平衡，常见方法是采用重新加权的CE损失或改进的Dice损失，以将训练聚焦于难分类区域。现有修改方法计算成本高且效果有限。在本研究中，我们提出对Dice损失进行简单修改，计算成本极低。通过像素级调制项，我们利用Dice损失处理类别不平衡的有效性，同时处理难度不平衡。在三个常用医学分割任务上的结果表明，所提出的像素级调制Dice损失（PM Dice损失）优于其他旨在解决难度不平衡问题的方法。
英文摘要：
Class imbalance and the difficulty imbalance are the two types of data imbalance that affect the performance of neural networks in medical segmentation tasks. In class imbalance the loss is dominated by the majority classes and in difficulty imbalance the loss is dominated by easy to classify pixels. This leads to an ineffective training. Dice loss, which is based on a geometrical metric, is very effective in addressing the class imbalance compared to the cross entropy (CE) loss, which is adopted directly from classification tasks. To address the difficulty imbalance, the common approach is employing a re-weighted CE loss or a modified Dice loss to focus the training on difficult to classify areas. The existing modification methods are computationally costly and with limited success. In this study we propose a simple modification to the Dice loss with minimal computational cost. With a pixel level modulating term, we take advantage of the effectiveness of Dice loss in handling the class imbalance to also handle the difficulty imbalance. Results on three commonly used medical segmentation tasks show that the proposed Pixel-wise Modulated Dice loss (PM Dice loss) outperforms other methods, which are designed to tackle the difficulty imbalance problem.
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