可学习原型引导的多实例学习在多癌全片病理图像中检测三级淋巴结构

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Pengfei Xia , Dehua Chen , Huimin An , Kiat Shenq Lim , Xiaoqun Yang
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引用次数: 0

摘要

三级淋巴样结构(TLS)是在特定病理条件下形成的异位淋巴样聚集体,如慢性炎症和恶性肿瘤。它们在肿瘤微环境(TME)中的存在与患者预后和对免疫治疗的反应密切相关,因此在全片病理图像(wsi)中检测TLS对临床决策至关重要。尽管多实例学习(MIL)在肿瘤微环境研究中显示出前景,但其在TLS检测中的潜力却受到了有限的关注。此外,wsi中TLS的稀疏性和异质性给特征提取带来了重大挑战,并限制了MIL在不同癌症类型中的推广。为了解决这个问题,本文提出了一个弱监督框架,可学习原型引导多实例学习(LPGMIL)。从TLS的细胞组成来看,LPGMIL选择淋巴细胞密集的实例构建可学习的全局原型,在训练过程中逐渐调整全局原型,以关注TLS相关特征。此外,LPGMIL使用多个可学习的全局原型计算每个WSI,有效地捕获各种TLS病理模式并精炼复杂TLS特征的表示。与以往在单一癌症类型数据集上评估的方法不同,我们整合了六种癌症类型的TCGA数据集,以更好地反映现实世界临床病例的多样性和复杂性。实验结果和可视化结果表明,LPGMIL在六种癌症类型的TCGA数据集上优于其他比较方法,达到76.6%的准确率,74.1%的召回率,82.7%的f1得分和83.5%的AUC,证明了其在解决TLS的稀疏性和异质性方面的有效性。代码可从https://github.com/FPXMU/LPGMIL获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learnable prototype-guided multiple instance learning for detecting tertiary lymphoid structures in multi-cancer whole-slide pathological images
Tertiary lymphoid structures (TLS) are ectopic lymphoid aggregates that form under specific pathological conditions, such as chronic inflammation and malignancies. Their presence within the tumor microenvironment (TME) is strongly correlated with patient prognosis and response to immunotherapy, making TLS detection in whole-slide pathological images (WSIs) crucial for clinical decision-making. Although multiple instance learning (MIL) has shown promise in tumor microenvironment studies, its potential for TLS detection has received limited attention. Additionally, the sparsity and heterogeneity of TLS in WSIs present significant challenges for feature extraction and limit the generalizability of MIL across different cancer types. To address this issue, this paper proposes a weakly supervised framework, Learnable Prototype-Guided Multiple Instance Learning (LPGMIL). From the perspective of the cellular composition of TLS, LPGMIL selects lymphocyte-dense instances to construct learnable global prototypes that are gradually adjusted during training to focus on TLS-related features. Additionally, LPGMIL computes each WSI using multiple learnable global prototypes, effectively capturing diverse TLS pathological patterns and refining the representation of complex TLS features. Unlike previous methods evaluated on single cancer-type datasets, we integrate a six-cancer-type TCGA dataset to better reflect the diversity and complexity of real-world clinical cases. Experimental results and visualizations show that LPGMIL outperforms other compared methods on a six-cancer-type TCGA dataset, achieving 76.6 % accuracy, 74.1 % recall, 82.7 % F1-score, and 83.5 % AUC, demonstrating its effectiveness in addressing the sparsity and heterogeneity of TLS. Code is available at: https://github.com/FPXMU/LPGMIL.
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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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