Learning from certain regions of interest in medical images via probabilistic positive-unlabeled networks

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Le Yi, Lei Zhang, Kefu Zhao, Xiuyuan Xu
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引用次数: 0

Abstract

The laborious annotation process and inherent image ambiguity exacerbate difficulties of data acquisition for medical image segmentation, leading to suboptimal performance in practice. This paper proposes a workaround against these challenges aiming to learn unbiased models solely from certainties. Concretely, during the labeling stage, only regions of interest confidently discerned by annotators are required to be labeled, not only increasing label quantity but also improving label quality. This approach formulates the positive-unlabeled (PU) segmentation problem and motivates to capture uncertainty in ambiguous regions. We thus delve into data-generating assumptions in the PU segmentation context and propose Probabilistic PU Segmentation Networks (ProPU-Nets) to tackle problems abovementioned. This framework employs the expectation–maximization algorithm to gradually estimate true masks, and more importantly, by encoding plausible segmentation variants in a latent space, uncertainty estimation can be naturally embedded into the PU segmentation framework. Benefitting from the framework’s unbiasedness, a semi-supervised PU segmentation method is also proposed, which can further excavate performance gains from unlabeled data. We conduct extensive experiments on LIDC, RIGA, and LA datasets, and comprehensively compared with state-of-the-art methods in label-efficient medical image segmentation. The results justify the method’s effectiveness and practical prospect.
通过概率正无标记网络从医学图像中感兴趣的特定区域学习。
繁琐的标注过程和固有的图像模糊性加剧了医学图像分割的数据获取困难,导致实际应用中的性能不理想。本文提出了一种解决这些挑战的方法,旨在仅从确定性中学习无偏模型。具体来说,在标注阶段,只需要标注标注者自信地识别出感兴趣的区域,既增加了标签数量,又提高了标签质量。该方法提出了正未标记(PU)分割问题,并激励捕获模糊区域的不确定性。因此,我们深入研究了PU分割上下文中的数据生成假设,并提出了概率PU分割网络(ProPU-Nets)来解决上述问题。该框架采用期望最大化算法逐步估计真实掩模,更重要的是,通过在潜在空间中编码合理的分割变量,不确定性估计可以自然嵌入到PU分割框架中。利用框架的无偏性,提出了一种半监督的PU分割方法,可以进一步挖掘未标记数据的性能收益。我们在LIDC、RIGA和LA数据集上进行了广泛的实验,并与最先进的标签高效医学图像分割方法进行了全面比较。结果证明了该方法的有效性和应用前景。
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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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