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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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可学习的原型导向多重实例学习,用于检测多种癌症全幻灯片病理图像中的三级淋巴状结构.

Pengfei Xia¹, Dehua Chen¹, Huimin An²

¹College of Computer Science and Technology, Donghua University, Shanghai 201620, China.

Medical image analysis

|May 30, 2025

概括

癌症图像中的三级淋巴体结构 (TLS) 检测得到了新的框架,LPGMIL的改进. 这种方法有效地识别了稀疏和多样化的TLS,提高了预后预测和免疫治疗反应评估.

关键词:

可学习的原型.多个实例的学习是多个实例的学习.三级淋巴体结构的第三级淋巴体结构.整个幻灯片病理图像的病理图像.

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科学领域:

病理学病理学病理学
计算生物学计算生物学
医疗成像医学成像

背景情况:

三级淋巴体结构 (TLS) 在瘤微环境 (TME) 中至关重要,影响患者的预后和免疫治疗反应.
在全幻灯片病理图像 (WSIs) 中精确的TLS检测对于临床决策至关重要.
现有的多实例学习 (MIL) 方法在检测稀疏和异质的TLS方面存在局限性.

研究的目的:

开发一个弱监督的框架,在WSIs中进行强大的TLS检测.
解决各种癌症类型中TLS稀疏性和异质性的挑战.
提高MIL在不同恶性瘤中进行TLS分析的通用性.

主要方法:

拟议的可学习原型导向多级学习 (LPGMIL) 框架.
利用淋巴细胞密集实例来创建可学习的全球原型,以改进功能.
采用多个可学习的全球原型来捕捉WSIs中的各种TLS模式.
验证了对六种癌症类型的全面TCGA数据集的框架.

主要成果:

与现有方法相比,LPGMIL在多种癌症数据集上表现优越.
获得了高精度 (76.6%),回忆 (74.1%),F1得分 (82.7%) 和AUC (83.5%).
在WSIs中有效处理TLS的稀疏性和异质性.

结论:

在复杂的癌症数据集中,LPGMIL提供了一种有效的解决方案,用于弱监督的TLS检测.
该框架增强了TLS的分析,这对于预测患者的结果和治疗疗效至关重要.
这种方法推进了精密瘤学的计算病理学.