This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we present a new formulation of pseudo-labelling as an Expectation-Maximization (EM) algorithm for clear statistical interpretation. Secondly, we propose a semi-supervised medical image segmentation method purely based on the original pseudo labelling, namely SegPL. We demonstrate SegPL is a competitive approach against state-of-the-art consistency regularisation based methods on semi-supervised segmentation on a 2D multi-class MRI brain tumour segmentation task and a 3D binary CT lung vessel segmentation task. The simplicity of SegPL allows less computational cost comparing to prior methods. Thirdly, we demonstrate that the effectiveness of SegPL may originate from its robustness against out-of-distribution noises and adversarial attacks. Lastly, under the EM framework, we introduce a probabilistic generalisation of SegPL via variational inference, which learns a dynamic threshold for pseudo labelling during the training. We show that SegPL with variational inference can perform uncertainty estimation on par with the gold-standard method Deep Ensemble.
翻译:本文涉及在分离中的假标签。 我们的贡献是四重。 首先, 我们提出一个新的伪标签配方, 作为一种预期- 最大化算法, 用于清晰的统计解释。 第二, 我们提议一种半监督的医疗图像分解方法, 纯粹基于原始的假标签, 即SegPL。 我们证明SegPL是一种竞争办法, 对抗基于半监督的半监督分解法, 即基于2D多级MRI脑肿瘤分解和3D二进制肺容器分解任务的最先进一致方法。 SegPL的简单化使得计算成本比以前的方法要低。 第三, 我们证明SegPL的效力可能源于其抗分配外噪音和对抗性攻击的稳健性。 最后, 在EM框架之下, 我们引入了一种通过变异推法对SegPL的概率性总体化方法, 在培训期间学习假标签的动态阈值。 我们证明, 具有变推推推法的SegPL可以对等值进行不确定性的估计。