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.
Abstract (translated)
全滑动图像（WSIs）对于癌症诊断至关重要，因为它们具有超高分辨率和丰富的语义内容。然而，由于其庞大的大小以及细粒度注释的有限可用性，传统监督学习面临着重大挑战。我们提出了DSAGL（双流注意力引导学习），这是一种新型弱监督分类框架，结合了教师-学生架构与双流设计。DSAGL通过生成基于多尺度注意力的伪标签并指导实例级学习来明确解决实例层面的模糊性和袋层级的语义一致性问题。一个共享的轻量级编码器（VSSMamba）能够有效地进行长程依赖建模，而融合注意模块（FASA）则增强了对稀疏但诊断相关的区域的关注。我们进一步引入了一种混合损失函数来强制执行两流之间的相互一致性。在CIFAR-10、NCT-CRC和TCGA-Lung数据集上的实验表明，DSAGL始终优于最先进的MIL基准模型，在弱监督条件下实现了优异的判别性能和鲁棒性。
URL
https://arxiv.org/abs/2505.23341
