论文标题
学习校准半监督分割的形态特征扰动
Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation
论文作者
论文摘要
我们提出了不匹配，这是一种新型的一致性驱动的半监督分割框架，该框架产生了对学习的特征扰动不变的预测。不匹配由编码器和两个头解码器组成。一个解码器在未标记的图像上学习了对兴趣的前景区域（ROI）的积极关注，从而产生了扩张的特征。另一个解码器在同一未标记的图像上学会了对前景的负面注意，从而产生了侵蚀的特征。然后，我们对配对预测进行一致性正则化。不匹配优于基于CT的肺部血管分割任务和基于MRI的脑肿瘤分割任务的最先进的半监督方法。此外，我们表明，不匹配的有效性来自比其监督学习的更好的模型校准。
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produces predictions that are invariant to learnt feature perturbations. MisMatch consists of an encoder and a two-head decoders. One decoder learns positive attention to the foreground regions of interest (RoI) on unlabelled images thereby generating dilated features. The other decoder learns negative attention to the foreground on the same unlabelled images thereby generating eroded features. We then apply a consistency regularisation on the paired predictions. MisMatch outperforms state-of-the-art semi-supervised methods on a CT-based pulmonary vessel segmentation task and a MRI-based brain tumour segmentation task. In addition, we show that the effectiveness of MisMatch comes from better model calibration than its supervised learning counterpart.
