重庆大学学报2026，Vol.49Issue(8)：101-112,12.DOI:10.11835/j.issn.1000-582X.2026.08.009
卵巢癌病理图像的单边噪声多实例学习分类方法
A one-sided label noise multi-instance learning classification method for ovarian cancer pathological images
摘要
Abstract
Ovarian cancer pathological tissues exhibit high morphological heterogeneity and complex microenvi-ronmental features,with benign and malignant regions often showing subtle and blurred boundaries visually,pos-ing significant challenges to pathological diagnosis.High annotation costs and one-sided label noise,where be-nign samples are mislabeled as malignant,pose major challenges in digital pathology.To address these issues,this study proposes a one-sided label noise multi-instance learning(MIL-OSLN)framework based on a dual-weighting mechanism to improve the automated diagnostic accuracy of ovarian cancer pathological images.The proposed framework dynamically assigns weights at both the instance and bag levels.This strategy enables the model to iden-tify diagnostically relevant regions from noisy data while suppressing interference from false-positive bags.The model was trained on a weakly labeled dataset generated via semi-supervised clustering,and its performance was evaluated against multiple baseline models using an independent gold-standard test set.Experimental results indi-cated that the model maintained a stable area under the curve(AUC)of approximately 0.9 even when the training set noise rate reached 50％.Its overall performance significantly outperformed all comparative baseline models.These findings demonstrate that the proposed method effectively addresses one-sided label noise in multi-instance learning and is well suited for intelligent tumor diagnosis using large-scale weakly labeled pathological data.关键词
医学成像/卵巢肿瘤/图像分类/多实例学习/标签噪声/病理图像分析Key words
medical imaging/ovarian tumors/image classification/multi-instance learning/label noise/pathology image analysis分类
信息技术与安全科学引用本文复制引用
戚伟豪,史珈硕,张宗梅,陶海波,金怀平..卵巢癌病理图像的单边噪声多实例学习分类方法[J].重庆大学学报,2026,49(8):101-112,12.基金项目
云南省兴滇英才支持计划项目,(KKRD202203073)云南省高校工业智能与系统重点实验室建设项目,(KKPH202403003).Yunnan Xingdian Talent Support Program(KKRD202203073),Construction Project of the Yunnan Provincial Key Laboratory of Industrial Intelligence and Systems for Universities(KKPH202403003). （KKRD202203073）
