arXiv：cs.LG· Chen Shu, Chengyang Zhang, Boyu Fu, Ting Yin, Wenchuan Zhang, Jie Chen, Yuhao Yi, Hong Bu·· 10 小时前
ProtoPur：面向病理弱监督实例学习的原型纯度伪标签方法
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology
AI 导读
ProtoPur 是一种面向病理全切片图像的弱监督实例级学习方法，通过在冻结基础模型特征构建的原型区域上估计类平衡局部纯度，为与目标类别强关联的高纯度区域分配伪标签。该方法在三个公开全切片图像数据集和两种病理基础编码器上，跨多种 MIL 骨干一致提升实例级性能，同时保持有竞争力的包级性能。论文已被 IEEE BIBM 2026 接收。
正文
Abstract:Weak bag-level labels are widely available in computational pathology, whereas reliable instance-level annotations for whole-slide images remain expensive and ambiguous. Weakly supervised multiple instance learning (MIL) achieves strong bag-level classification, but instance-level supervision from attention scores, model predictions, negative bags, or spatial refinement may fail to identify class-specific local evidence. We propose ProtoPur, a prototype-purity pseudo-labeling method for weakly supervised instance-level learning in pathology whole-slide images. ProtoPur estimates class-balanced local purity over prototype regions constructed from frozen foundation-model features, and assigns pseudo-labels to high-purity regions strongly associated with a target class. This purity-first design separates class-specific evidence from negative or shared morphology, with coverage used only to ensure sufficient pseudo-label assignment. We provide a PAC-style analysis showing that the risk of an instance classifier trained with ProtoPur pseudo-labels can be decomposed into a standard uniform-convergence term and a ProtoPur-specific pseudo-label noise term, comprising selected-region mismatch and a coverage residual. Experiments on three public whole-slide image datasets with two pathology foundation encoders show that ProtoPur consistently improves instance-level performance across multiple MIL backbones while maintaining competitive bag-level performance. Pseudo-label quality comparisons on two public datasets further demonstrate that prototype-purity selection provides stronger instance supervision. These results suggest that the foundation-model features contain exploitable instance-level supervision signals, and ProtoPur is a way to reveal and use these signals from weak bag labels without requiring dense instance annotations.
| Comments: | Accepted to IEEE BIBM 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.15669 [cs.CV] |
| (or arXiv:2610.15669v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15669 arXiv-issued DOI via DataCite |
Submission history
From: Chen Shu [view email]
[v1]
Sun, 11 Oct 2026 14:47:50 UTC (5,241 KB)
[v2]
Wed, 14 Oct 2026 16:16:48 UTC (5,241 KB)
来源：arXiv：cs.LG · arxiv.org