Probability calibration for deep models is highly desirable in safety-critical applications such as medical imaging. It makes output probabilities of deep networks interpretable, by aligning prediction probability with the actual accuracy in test data. In image segmentation, well-calibrated probabilities allow radiologists to identify regions where model-predicted segmentations are unreliable. These unreliable predictions often occur to out-of-domain (OOD) images that are caused by imaging artifacts or unseen imaging protocols. Unfortunately, most previous calibration methods for image segmentation perform sub-optimally on OOD images. To reduce the calibration error when confronted with OOD images, we propose a novel post-hoc calibration model. Our model leverages the pixel susceptibility against perturbations at the local level, and the shape prior information at the global level. The model is tested on cardiac MRI segmentation datasets that contain unseen imaging artifacts and images from an unseen imaging protocol. We demonstrate reduced calibration errors compared with the state-of-the-art calibration algorithm.

翻译：深海模型的概率校准在医学成像等安全关键应用中非常可取。 它通过将预测概率与测试数据的实际准确性相匹配,使深网络的输出概率可以解释。 在图像分解中,精确校准的概率使放射学家能够确定模型预测的分解不可靠的区域。 这些不可靠的预测经常发生在由成像成像成像或视觉成像协议造成的外部图像中。 不幸的是,大多数以前的图像分解校准方法在OOD图像上表现了亚最佳性。 为了减少对OOOD图像的校准误差,我们提议了一个新的热后校准模型。我们的模型利用像素感应力来防止地方一级的扰动,以及全球一级的形状信息。模型在含有视觉成像成像协议和图像的心力MRI分解数据集上进行测试。 我们显示,与最先进的校准算算法相比,校准错误有所减少。