超越标量损失：通过梯度向量场手术校准分割模型
Beyond scalar losses: calibrating segmentation models via gradient vector field surgery
- School of Computation, Information and Technology, TUM（慕尼黑工业大学计算、信息与技术学院）
- Munich Center for Machine Learning（慕尼黑机器学习中心）
- Department of Radiology, Weill Cornell Medicine（威尔康乃尔医学院放射科）
- School of Medicine and Health, TUM University Hospital（慕尼黑工业大学医院医学与健康学院）
- Cornell Tech（康奈尔科技学院）
- Department of Computing, Imperial College London（伦敦帝国理工学院计算系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对基于区域损失函数训练的分割模型校准不佳问题，提出对梯度向量场进行“手术”，即给损失偏导数添加因子，依预测误差线性缩放梯度大小，经2D和3D医学分割任务验证该方法有效且能保持高预测准确性。
AI中文摘要：
基于区域的损失函数，如骰子损失，已成为高度类别和区域不平衡分割任务的事实上的标准。然而，使用基于区域的损失函数训练的模型校准不佳，通常会产生过度自信的预测。在医学成像应用中，这种校准错误阻碍了临床应用。在这项工作中，我们概述了一种关于这种过度自信的新梯度观点，并展示了它如何影响基于区域的损失函数。我们提出对梯度向量场进行“手术”，作为一种简单而有效的干预措施来减轻校准问题。这种手术在损失的偏导数中添加一个因子，根据预测误差线性缩放梯度的大小。在2D和3D医学分割任务的实证评估中，我们证明了这种干预的有效性，同时当与任何基于区域的损失函数结合使用时保持高预测准确性。
英文摘要：
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.
