通过调整梯度场，解决医学图像分割模型过自信问题。
Beyond scalar losses: calibrating segmentation models via gradient vector field surgery
- 在损失函数梯度上添加误差相关缩放因子，调节模型信心
- 3D/2D医疗分割任务中显著改善校准性，精度不变
- 适合对预测置信度敏感的临床应用，如肿瘤边界界定
基于区域的损失函数（如Dice损失）已成为处理高度类别和区域不平衡分割任务的默认选择。然而，使用这类损失函数训练的模型通常存在校准不足问题，产生过自信的预测。在医学影像应用中（如定义肿瘤切除边界），这种校准偏差阻碍了其临床应用。本文从梯度角度分析了该过自信现象，并提出一种简单的梯度向量场“手术”方法：在损失的偏导数中引入与预测误差线性相关的缩放因子，从而调节梯度大小。在2D和3D医疗分割任务上的实证评估表明，该方法可有效缓解校准问题，同时保持与任意基于区域的损失函数结合时的高预测精度。
原文摘要 · Abstract (English)
Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced segmentation tasks. However, models trained using region-based loss functions are notoriously miscalibrated and typically yield over-confident predictions. In medical imaging applications, such as defining tumor resection margins, this miscalibration is hindering clinical adoption. In this work, we outline a novel gradient perspective on this overconfidence and show how it affects region-based loss functions. We propose a "surgery" on the gradient vector field as a simple, yet effective intervention to mitigate calibration issues. This surgery adds a factor to the loss's partial derivative, scaling the gradient's magnitude linearly with the prediction error. In empirical evaluations across 2D and 3D medical segmentation tasks, we demonstrate the effectiveness of this intervention while maintaining high prediction accuracy when used in conjunction with any region-based loss function.
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