尽管多实例学习 (MIL) 方法广泛用于整个幻灯片图像 (WSI) 上的自动肿瘤检测，但它们遭受包含小肿瘤的极端类别不平衡 WSI，其中肿瘤可能仅包含几个孤立的细胞。对于早期检测，MIL 算法能够识别小肿瘤非常重要。现有的研究尝试使用基于注意力的架构和基于实例选择的方法来解决这个问题，但尚未产生显着的改进。本文提出了基于交叉注意的显着性实例推理 MIL (CASiiMIL)，它涉及一种新颖的显着性通知注意机制，无需任何注释即可识别 WSI 上的小肿瘤（例如乳腺癌淋​​巴结微转移）。除了这种新的注意力机制之外，我们还引入了负表示学习算法，以促进显着性信息注意力权重的学习，从而提高对肿瘤 WSI 的敏感性。该模型在两个流行的肿瘤转移检测数据集上优于最先进的 MIL 方法。所提出的方法表现出良好的跨中心通用性、对具有小肿瘤病变的 WSI 进行分类的高精度以及归因于显着性通知的注意力权重的出色可解释性。我们期望所提出的方法将为在大型数据集上进行早期肿瘤检测的训练算法铺平道路，在大型数据集上获取细粒度注释是不切实际的。

Although multiple instance learning (MIL) methods are widely used for automatic tumor detection on whole slide images (WSI), they suffer from the extreme class imbalance WSIs containing small tumors where the tumor may include only a few isolated cells. For early detection, it is important that MIL algorithms can identify small tumors. Existing studies have attempted to address this issue using attention-based architectures and instance selection-based methodologies but have not produced significant improvements. This paper proposes crossattention-based salient instance inference MIL (CASiiMIL), which involves a novel saliency-informed attention mechanism to identify small tumors (e.g., breast cancer lymph node micro-metastasis) on WSIs without needing any annotations. In addition to this new attention mechanism, we introduce a negative representation learning algorithm to facilitate the learning of saliencyinformed attention weights for improved sensitivity on tumor WSIs. The proposed model outperforms the state-ofthe-art MIL methods on two popular tumor metastasis detection datasets. The proposed approach demonstrates great cross-center generalizability, high accuracy in classifying WSIs with small tumor lesions, and excellent interpretability attributed to the saliency-informed attention weights. We expect that the proposed method will pave the way for training algorithms for early tumor detection on large datasets where acquiring fine-grained annotations is is not practical.