Modern deep neural networks have achieved remarkable progress in medical image segmentation tasks. However, it has recently been observed that they tend to produce overconfident estimates, even in situations of high uncertainty, leading to poorly calibrated and unreliable models. In this work we introduce Maximum Entropy on Erroneous Predictions (MEEP), a training strategy for segmentation networks which selectively penalizes overconfident predictions, focusing only on misclassified pixels. In particular, we design a regularization term that encourages high entropy posteriors for wrong predictions, increasing the network uncertainty in complex scenarios. Our method is agnostic to the neural architecture, does not increase model complexity and can be coupled with multiple segmentation loss functions. We benchmark the proposed strategy in two challenging medical image segmentation tasks: white matter hyperintensity lesions in magnetic resonance images (MRI) of the brain, and atrial segmentation in cardiac MRI. The experimental results demonstrate that coupling MEEP with standard segmentation losses leads to improvements not only in terms of model calibration, but also in segmentation quality.
翻译:现代深层神经网络在医学图像分割任务方面取得了显著进展,但最近观察到,即使在高度不确定的情况下,它们往往产生过于自信的估计数,导致模型不精确和不可靠的模型。在这项工作中,我们引入了对不协调预测的最大反射(MEEP),这是对分解网络的培训战略,有选择地惩罚过度信任预测,只侧重于误分的像素。特别是,我们设计了一个正规化术语,鼓励对错误的预测使用高微粒子子子,增加复杂情景中网络的不确定性。我们的方法对神经结构来说是不可知的,不会增加模型复杂性,而且可以与多重分解损失功能相结合。我们将拟议战略作为两项具有挑战性的医疗图像分割任务的基准:脑磁共振图像中的白色物质超常度损害,以及心脏MRI的白分解。实验结果表明,与标准分解损失相结合的MEEP不仅在模型校准方面,而且还在分解质量上都会导致改进。