基于间不确定性与内不确定性的特征聚合模型用于半监督组织病理图像分割
Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
AI总结：
针对半监督组织病理图像分割中标注稀缺问题，提出利用间与内不确定性正则化及两阶段伪掩码引导特征聚合网络（PG-FANet），在MoNuSeg和CRAG数据集上超越现有方法。
摘要：
在组织学等需要领域专业知识的应用中，获取像素级标注往往受到限制。各种半监督学习方法已被开发出来，以在有限的地面真值标注下工作，例如流行的教师-学生模型。然而，现有方法尚未充分利用学生模型内的层次预测不确定性（内不确定性）和图像预测不确定性（间不确定性）。为了解决这些问题，我们首先提出了一种新的间不确定性和内不确定性正则化方法，用于衡量和约束教师-学生架构中的间不一致性和内不一致性。我们还提出了一种新的两阶段网络，即伪掩码引导特征聚合网络（PG-FANet），作为分割模型。两阶段结构与不确定性正则化策略互补，以避免在解决不确定性时引入额外模块，并且聚合机制能够实现多尺度和多阶段特征集成。在MoNuSeg和CRAG数据集上的综合实验结果表明，我们的PG-FANet优于其他最先进的方法，并且我们的半监督学习框架在有限数量的标注数据下取得了有竞争力的性能。
英文摘要：
Acquiring pixel-level annotations is often limited in applications such as histology studies that require domain expertise. Various semi-supervised learning approaches have been developed to work with limited ground truth annotations, such as the popular teacher-student models. However, hierarchical prediction uncertainty within the student model (intra-uncertainty) and image prediction uncertainty (inter-uncertainty) have not been fully utilized by existing methods. To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture. We also propose a new two-stage network with pseudo-mask guided feature aggregation (PG-FANet) as the segmentation model. The two-stage structure complements with the uncertainty regularization strategy to avoid introducing extra modules in solving uncertainties and the aggregation mechanisms enable multi-scale and multi-stage feature integration. Comprehensive experimental results over the MoNuSeg and CRAG datasets show that our PG-FANet outperforms other state-of-the-art methods and our semi-supervised learning framework yields competitive performance with a limited amount of labeled data.
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