CoLM: Contrastive learning and multiple instance learning network for lung cancer classification of surgical options based on frozen pathological images

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
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引用次数: 0

Abstract

Histopathological images are regarded as the gold standard in cancer diagnosis. Formalin-fixed paraffin-embedded (FFPE) tissues are routinely collected and archived for pathological examination. However, the time-consuming procedures of tissue fixation and embedding render FFPE tissues unsuitable for intraoperative diagnosis, where immediate results are crucial during surgical procedures. In contrast, obtaining a fresh frozen section (FS) takes a very short time. FS samples are widely utilized for intraoperative diagnosis, whereas the diagnostic accuracy of FS is currently limited by the presence of potential histological artifacts. In this paper, we propose a contrastive learning image translation and multiple instance learning network (CoLM) for lung cancer classification. CoLM efficiently translates FS images into FFPE-style images and facilitates whole slide image classification. The entire framework encompasses two crucial stages. In the first stage, we employ a contrastive learning translation network with a dual-attention module (CL-DAM) for image translation. In the second stage, we utilize a hybrid transformer multi-instance learning-based network (HTM) to address the challenge posed by weak labels. We conduct experiments on lung cancer datasets to validate the performance of our proposed approach. The results demonstrate that our method achieve superior classification performance over other state-of-the-art methods, effectively mitigating the impact of blurred FS images. The proposed framework not only elevates the precision of intraoperative diagnosis when employing FS but also provides valuable reference for pathologists through the application of synthetic images.
CoLM:基于冰冻病理图像的肺癌手术方案分类对比学习和多实例学习网络
组织病理学图像被视为癌症诊断的黄金标准。福尔马林固定石蜡包埋(FFPE)组织被常规收集和存档,用于病理检查。然而,由于组织固定和包埋过程耗时,FFPE 组织不适合用于术中诊断,因为在手术过程中,即时结果至关重要。相比之下,获取新鲜冷冻切片(FS)只需很短的时间。新鲜冰冻切片样本被广泛用于术中诊断,但由于存在潜在的组织学伪影,目前新鲜冰冻切片的诊断准确性受到限制。在本文中,我们提出了一种用于肺癌分类的对比学习图像翻译和多实例学习网络(CoLM)。CoLM 能有效地将 FS 图像转换为 FFPE 类型的图像,并促进整张切片图像的分类。整个框架包括两个关键阶段。在第一阶段,我们采用带有双注意模块(CL-DAM)的对比学习翻译网络进行图像翻译。在第二阶段,我们利用基于多实例学习的混合转换器网络(HTM)来应对弱标签带来的挑战。我们在肺癌数据集上进行了实验,以验证我们提出的方法的性能。结果表明,与其他最先进的方法相比,我们的方法实现了更优越的分类性能,有效减轻了模糊 FS 图像的影响。所提出的框架不仅提高了使用 FS 时术中诊断的精确度,还通过合成图像的应用为病理学家提供了有价值的参考。
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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