核分割的自适应特征聚合网络

Ruizhe Geng, Zhongyi Huang, Jie Chen
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引用次数: 0

摘要

细胞核实例分割是细胞形态计量学和分析的基础,在数字病理学中起着至关重要的作用。不同细胞类型的细胞核特征的可变性问题使这项任务更具挑战性。近年来,基于提议的特征金字塔网络(FPN)分割方法由于融合了多尺度特征,具有较强的语义特征,表现出了较好的分割效果。然而,FPN存在最高级特征映射的信息丢失和次优特征融合策略。为了充分利用多尺度特征,提出了一种基于提议的自适应特征聚合方法(AANet)。具体来说,AANet由两个部分组成:上下文增强模块(Context Augmentation Module, CAM)和特征自适应选择模块(Feature Adaptive Selection Module, ASM)。在特征融合中,CAM侧重于挖掘广泛的上下文信息和捕获判别语义,以减少最高金字塔层特征图的信息丢失。然后将增强的特征发送给ASM,以自适应地获得每个RoI的所有特征级别的组合特征表示。实验证明了我们的模型在两个公开可用的数据集上的有效性:Kaggle 2018数据科学碗数据集和多器官核分割数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive feature aggregation network for nuclei segmentation
Nuclei instance segmentation is essential for cell morphometrics and analysis, playing a crucial role in digital pathology. The problem of variability in nuclei characteristics among diverse cell types makes this task more challenging. Recently, proposal-based segmentation methods with feature pyramid network (FPN) has shown good performance because FPN integrates multi-scale features with strong semantics. However, FPN has information loss of the highest-level feature map and sub-optimal feature fusion strategies. This paper proposes a proposal-based adaptive feature aggregation methods (AANet) to make full use of multi-scale features. Specifically, AANet consists of two components: Context Augmentation Module (CAM) and Feature Adaptive Selection Module (ASM). In feature fusion, CAM focus on exploring extensive contextual information and capturing discriminative semantics to reduce the information loss of feature map at the highest pyramid level. The enhanced features are then sent to ASM to get a combined feature representation adaptively over all feature levels for each RoI. The experiments show our model's effectiveness on two publicly available datasets: the Kaggle 2018 Data Science Bowl dataset and the Multi-Organ nuclei segmentation dataset.
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