DSAGL: Dual-Stream Attention-Guided Learning for Weakly Supervised Whole Slide Image Classification

• URL: http://arxiv.org/abs/2505.23341v2
• Date: Fri, 27 Jun 2025 07:34:36 GMT
• Title: DSAGL: Dual-Stream Attention-Guided Learning for Weakly Supervised Whole Slide Image Classification
• Authors: Daoxi Cao, Hangbei Cheng, Yijin Li, Ruolin Zhou, Xuehan Zhang, Xinyi Li, Binwei Li, Xuancheng Gu, Jianan Zhang, Xueyu Liu, Yongfei Wu,
• Abstract summary: Whole-slide images (WSIs) are critical for cancer diagnosis due to their ultra-high resolution and rich semantic content.<n>We propose DSAGL (Dual-Stream Attention-Guided Learning), a novel weakly supervised classification framework that combines a teacher-student architecture with a dual-stream design.
• Score: 5.260725801393189
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Whole-slide images (WSIs) are critical for cancer diagnosis due to their ultra-high resolution and rich semantic content. However, their massive size and the limited availability of fine-grained annotations pose substantial challenges for conventional supervised learning. We propose DSAGL (Dual-Stream Attention-Guided Learning), a novel weakly supervised classification framework that combines a teacher-student architecture with a dual-stream design. DSAGL explicitly addresses instance-level ambiguity and bag-level semantic consistency by generating multi-scale attention-based pseudo labels and guiding instance-level learning. A shared lightweight encoder (VSSMamba) enables efficient long-range dependency modeling, while a fusion-attentive module (FASA) enhances focus on sparse but diagnostically relevant regions. We further introduce a hybrid loss to enforce mutual consistency between the two streams. Experiments on CIFAR-10, NCT-CRC, and TCGA-Lung datasets demonstrate that DSAGL consistently outperforms state-of-the-art MIL baselines, achieving superior discriminative performance and robustness under weak supervision.

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