提出一种轻量级损失函数，同时解决医学图像分割中的类别与难度不平衡问题。
Pixel-wise Modulated Dice Loss for Medical Image Segmentation
- 在像素级别引入调制项，动态调整损失权重
- 在三个数据集上均优于现有方法，提升分割精度
- 计算开销极低，适合实际医疗场景应用
类别不平衡和难度不平衡是影响医学图像分割中神经网络性能的两类数据失衡问题。类别不平衡导致多数类主导损失，而难度不平衡则使易分类像素占据训练主导，造成训练无效。相较于直接从分类任务沿用的交叉熵损失，基于几何度量的Dice损失在缓解类别不平衡方面表现优异。为应对难度不平衡，现有方法多采用重加权交叉熵或修改Dice损失，但普遍存在计算成本高、效果有限的问题。本文提出一种简单有效的Dice损失改进方法——像素级调制Dice损失（PM Dice loss），通过在像素级别引入调制项，在保留原有对类别不平衡良好处理能力的基础上，进一步抑制易分类区域的影响，聚焦难分区域。在三个常用医学图像分割任务上的实验表明，所提方法显著优于针对难度不平衡设计的其他方法。
原文摘要 · Abstract (English)
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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