基于边界样本的医学影像恶性肿瘤分类类加权半监督学习。

IF 2.6 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Pei Fang, Renwei Feng, Changdong Liu, Renjun Wen
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引用次数: 0

摘要

医学图像分类在医学领域发挥着举足轻重的作用。现有模型主要依赖于监督学习方法,这种方法需要大量标注数据才能进行有效训练。然而,获取和标注医学图像数据既昂贵又耗时。相比之下,半监督学习方法提供了一种很有前景的方法,即利用有限的标记数据和大量的非标记数据来提高医学图像分类的性能。然而,由于自生成伪标签中固有的噪声以及不同类别的边界样本的存在,目前的方法经常会遇到确认偏差。为了克服这些挑战,本研究引入了一种用于医学图像分类的新型框架,即基于边界样本的类加权半监督学习(BSCSSL)。我们的方法旨在减轻来自未标记数据的类内和类间边界样本的影响。具体来说,我们通过使用类间边界样本挖掘模块,分别处理可靠的机密数据和类间边界样本。此外,我们还实施了一种类内边界样本加权机制,以提取类内边界样本特有的类感知特征。我们的方法不会直接丢弃这些类内边界样本,而是承认它们的内在价值,尽管它们在准确分类方面存在困难,因为它们对模型预测有重大贡献。在广泛认可的医学图像数据集上的实验结果表明,我们提出的 BSCSSL 方法优于现有的半监督学习方法。通过提高医学影像分类的准确性和鲁棒性,我们的 BSCSSL 方法对推动医学诊断和未来研究工作具有重大意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Boundary sample-based class-weighted semi-supervised learning for malignant tumor classification of medical imaging.

Boundary sample-based class-weighted semi-supervised learning for malignant tumor classification of medical imaging.

Medical image classification plays a pivotal role within the field of medicine. Existing models predominantly rely on supervised learning methods, which necessitate large volumes of labeled data for effective training. However, acquiring and annotating medical image data is both an expensive and time-consuming endeavor. In contrast, semi-supervised learning methods offer a promising approach by harnessing limited labeled data alongside abundant unlabeled data to enhance the performance of medical image classification. Nonetheless, current methods often encounter confirmation bias due to noise inherent in self-generated pseudo-labels and the presence of boundary samples from different classes. To overcome these challenges, this study introduces a novel framework known as boundary sample-based class-weighted semi-supervised learning (BSCSSL) for medical image classification. Our method aims to alleviate the impact of intra- and inter-class boundary samples derived from unlabeled data. Specifically, we address reliable confidential data and inter-class boundary samples separately through the utilization of an inter-class boundary sample mining module. Additionally, we implement an intra-class boundary sample weighting mechanism to extract class-aware features specific to intra-class boundary samples. Rather than discarding such intra-class boundary samples outright, our approach acknowledges their intrinsic value despite the difficulty associated with accurate classification, as they contribute significantly to model prediction. Experimental results on widely recognized medical image datasets demonstrate the superiority of our proposed BSCSSL method over existing semi-supervised learning approaches. By enhancing the accuracy and robustness of medical image classification, our BSCSSL approach yields considerable implications for advancing medical diagnosis and future research endeavors.

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来源期刊
Medical & Biological Engineering & Computing
Medical & Biological Engineering & Computing 医学-工程:生物医学
CiteScore
6.00
自引率
3.10%
发文量
249
审稿时长
3.5 months
期刊介绍: Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging. MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field. MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).
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