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基于深度学习的犬类扩散型大B细胞淋巴瘤的形态细分.

Kenneth Ancheta¹, Androniki Psifidi², Andrew D Yale²

¹Pathobiology and Population Science, Royal Veterinary College, Hatfield, United Kingdom.

Frontiers in veterinary science

|September 10, 2025

概括

一个新的AI工具,HawksheadNet,准确地使用整个幻灯片图像来区分犬类扩散性大B细胞淋巴瘤 (cDLBCL) 和良性疾病. 这种卷积神经网络 (CNN) 方法有助于兽医诊断,提高淋巴瘤检测的准确性和效率.

关键词:

人工智能的人工智能是人工智能.狗类犬类动物狗类犬类深度学习是一种深度学习.数字病理学数字病理学淋巴瘤淋巴瘤是什么

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科学领域:

兽医病理学兽医病理学
计算病理学计算病理学
人工智能在医学中的应用

背景情况:

扩散性大B细胞淋巴瘤 (DLBCL) 是人类和狗的常见癌症.
狗DLBCL (cDLBCL) 是具有侵略性的,目前的诊断依赖于耗时的组织病理学.
在兽医中需要更快,更准确的诊断工具.

研究的目的:

开发和评估一个卷积神经网络 (CNN) 用于区分cDLBCL与反应性淋巴细胞增生 (RLH) 在狗淋巴结活检中.
介绍HawksheadNet,一种新的CNN架构用于癌症图像分类.
评估斑点正常化对CNN性能的影响.

主要方法:

整个幻灯片图像 (WSIs) 的H&E染色犬类淋巴结被数字化.
用于图像预处理,使用了修改后的阿根廷协议.
头网 (HawksheadNet) 是一个轻量级的CNN,在一个分为训练,验证和测试集的数据集上进行训练和微调.
使用StainNet应用了污点正常化.

主要成果:

头网络在StainNet规范化图像上实现了0.9691的接收器操作特征 (AUROC) 下的高面积,以区分cDLBCL与RLH.
美国有线电视新闻网 (CNN) 的表现优于其他预先训练的模型,如EfficientNet,Inception和MobileNet.
使用式预测的WSI细分提供了与病理学家解释一致的视觉诊断辅助工具.

结论:

卷积神经网络,特别是头网络,显示出从WSIs获得准确cDLBCL诊断的巨大潜力.
斑点正常化对于优化兽医癌症图像分析中的CNN性能至关重要.
这种人工智能驱动的方法可以增强兽医诊断工作流程,可能改善患者护理和预后.