计算机科学
体素
人工智能
背景(考古学)
分割
模式识别(心理学)
代表(政治)
特征向量
对比度(视觉)
特征(语言学)
水准点(测量)
特征学习
图像(数学)
集合(抽象数据类型)
语言学
古生物学
法学
生物
程序设计语言
地理
哲学
大地测量学
政治
政治学
作者
Chenyu You,Ruihan Zhao,Lawrence H. Staib,James S. Duncan
标识
DOI:10.1007/978-3-031-16440-8_61
摘要
Contrastive learning (CL) aims to learn useful representation without relying on expert annotations in the context of medical image segmentation. Existing approaches mainly contrast a single positive vector (i.e., an augmentation of the same image) against a set of negatives within the entire remainder of the batch by simply mapping all input features into the same constant vector. Despite the impressive empirical performance, those methods have the following shortcomings: (1) it remains a formidable challenge to prevent the collapsing problems to trivial solutions; and (2) we argue that not all voxels within the same image are equally positive since there exist the dissimilar anatomical structures with the same image. In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions. Specifically, we first introduce a novel CL strategy to ensure feature diversity promotion among the 3D representation dimensions. We train the framework through bi-level contrastive optimization (i.e., low-level and high-level) on 3D images. Experiments on two benchmark datasets and different labeled settings demonstrate the superiority of our proposed framework. More importantly, we also prove that our method inherits the benefit of hardness-aware property from the standard CL approaches. Codes will be available soon.
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