Dual-stream attention-guided learning for weakly supervised whole slide image classification

Daoxi Cao; Hangbei Cheng; Yijin Li; Ruolin Zhou; Xuehan Zhang; Xinyi Li; Binwei Li; Xuancheng Gu; Jianan Zhang; Xueyu Liu; Yongfei Wu

arXiv · 2505.23341

Dual-stream attention-guided learning for weakly supervised whole slide image classification

Daoxi Cao·Hangbei Cheng·Yijin Li·Ruolin Zhou·Xuehan Zhang·Xinyi Li·Binwei Li·Xuancheng Gu·Jianan Zhang·Xueyu Liu·Yongfei Wu

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Abstract

Whole slide images (WSIs) play a crucial role in cancer diagnosis due to their ultra-high resolution and rich morphological information, and multiple instance learning (MIL) has become a prevalent paradigm to solve the massive size of WSIs and the scarcity of fine-grained annotations of instance. However, most existing MIL methods struggle to accurately identify diagnostically critical local regions (instance) using only slide-level labels, and suffer from modelling the relationship of instances efficiently. To address these defects, we propose a Dual-Stream Attention-Guided Learning (DSAGL) framework. DSAGL bridges slide-level supervision and instance-level learning through a teacher-student dual-stream architecture, and mitigates instance ambiguity by generating attention-guided pseudo labels. The framework employs a shared lightweight encoder to efficiently model long-range dependencies and an attention-based fusion mechanism to enhance sensitivity to sparse, informative regions. Extensive experiments on synthetic benchmarks and real-world pathological WSI datasets demonstrate that DSAGL consistently outperforms state-of-the-art MIL methods, achieving superior discriminative performance and robustness under weak supervision.

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Daoxi Cao, Hangbei Cheng, Yijin Li, Ruolin Zhou, Xuehan Zhang, Xinyi Li, Binwei Li, Xuancheng Gu, Jianan Zhang, Xueyu Liu, Yongfei Wu. 2025-05-29. Dual-stream attention-guided learning for weakly supervised whole slide image classification. https://arxiv.org/abs/2505.23341

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