基于谱介入的不变因果表示学习在单域广义医学图像分割中的应用

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wentao Liu , Zhiwei Ni , Xuhui Zhu , Qian Chen , Liping Ni , Pingfan Xia
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引用次数: 0

摘要

一个训练良好的分割模型的性能常常受到采集方差引起的域漂移的影响。现有的努力致力于扩大单源样本的多样性,以及学习域不变表示。从本质上讲,他们仍然在对样本-标签对之间的统计依赖性进行建模,以实现对现实的肤浅描述。相反,我们提出了一个基于频谱干预的不变因果表示学习(SI2CRL)框架,从因果的角度统一数据生成和表示学习。具体而言,在数据生成中,将未知目标元素在频域中具体化为相位变量,然后提出基于幅值的干预模块,通过随机加权多层卷积网络产生低频扰动。对于因果表示,提出了一个两阶段的因果协同建模过程来推导不可观察的因果因素。在第一阶段,采用基于对比的因果解耦机制滤除编码器浅层的风格敏感非因果因素。在第二阶段,首先用交叉协方差正则化对解码器中的分层特征进行因式分解,以确保信道独立性;随后,我们引入了一个基于对抗性的因果净化模块,该模块鼓励解码器迭代更新因果充分的信息并做出领域鲁棒性预测。我们将SI2CRL与最先进的前列腺MRI跨部位分割、跨模态(CT-MRI)腹部多器官分割和跨序列(MRI)心脏分割方法进行比较。与同类方法相比,我们的方法实现了一致的性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spectrum intervention based invariant causal representation learning for single-domain generalizable medical image segmentation
The performance of a well-trained segmentation model is often trapped by domain shift caused by acquisition variance. Existing efforts are devoted to expanding the diversity of single-source samples, as well as learning domain-invariant representations. Essentially, they are still modeling the statistical dependence between sample-label pairs to achieve a superficial portrayal of reality. On the contrary, we propose a Spectrum Intervention based Invariant Causal Representation Learning (SI2CRL) framework, to unify the data generation and representation learning from causal view. Specifically, for the data generation, the unknown object elements can be reified in frequency domain as phase variables, then we propose an amplitude-based intervention module to generate low-frequency perturbations via random-weighted multilayer convolutional network. For the causal representations, a two-stage causal synergy modeling process is proposed to derive unobservable causal factors. In the first stage, the style-sensitive non-causal factors lying in the shallow layer of encoder are filtered out by contrastive-based causal decoupling mechanism. In the second stage, the hierarchical features in decoder are first factorized with cross-covariance regularization to ensure channel-wise independence; Subsequently, we introduce an adversarial-based causal purification module, which encourages the decoder to iteratively update causally sufficient information and make domain-robust predictions. We evaluate our SI2CRL against the state-of-the-art methods on cross-site prostate MRI segmentation, cross-modality (CT-MRI) abdominal multi-organ segmentation, and cross-sequence (MRI) cardiac segmentation. Our approach achieves consistent performance gains compared to these peer methods.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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