基于低置信度一致性和类分离的半监督医学图像分割

Zhimin Gao, Tianyou Yu
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引用次数: 0

摘要

深度学习在图像分类、语义分割等各个领域都取得了巨大的成功。但其优异的性能往往依赖于大量难以收集的数据注释,特别是在密集的预测任务中,如医学图像分割。半监督学习(SSL)作为一种流行的解决方案,减轻了标注的负担。然而,目前大多数半监督医学图像分割方法对每个像素都一视同仁,低估了难以区分的低比例像素的重要性,这些像素被容易区分的高比例像素所淹没。我们认为,这些较少被关注的区域往往包含了关键和不可或缺的信息,从而获得更好的分割性能。因此,我们提出了一种简单而有效的半监督医学图像分割方法,即增强低置信度一致性和应用低置信度类分离。具体来说,我们通过模型预测的最大概率值来分离低置信度像素和高置信度像素,只保留低置信度像素。对于这些剩余的像素,在平均教师框架中,在输出级别强制一致性来实现学生和教师之间的不变预测,并且在特征级别应用类分离来促进接近相应类原型的表示。我们在两个公开的心脏数据集上评估了所提出的方法,在这两个数据集上都取得了比最先进的半监督方法更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semi-supervised Medical Image Segmentation with Low-Confidence Consistency and Class Separation
Deep learning has achieved a great success in various fields, such as image classification, semantic segmentation and so on. But its excellent performance tends to rely on a large amount of data annotations that are hard to collect, especially in dense prediction tasks, like medical image segmentation. Semi-supervised learning (SSL), as a popular solution, relieves the burden of labeling. However, most of current semi-supervised medical image segmentation methods treat each pixel equally and underestimate the importance of indistinguishable and low-proportion pixels which are drowned in easily distinguishable but high-proportion pixels. We believe that these regions with less attention tend to contain crucial and indispensable information to obtain better segmentation performance. Therefore, we propose a simple but effective method for semi-supervised medical image segmentation task via enforcing low-confidence consistency and applying low-confidence class separation. Concretely, we separate low- and high-confidence pixels via the maximum probability values of model’s predictions and only low-confidence pixels are kept. For these remaining pixels, in the mean teacher framework, consistency is enforced for invariant predictions between student and teacher in the output level, and class separation is applied for promoting representations close to corresponding class prototypes in the feature level. We evaluated the proposed approach on two public datasets of cardiac, achieving a higher performance than the state-of-the-art semi-supervised methods on both datasets.
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