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 within the small tumor WSIs. This occurs when the tumor
comprises only a few isolated cells. For early detection, it is of utmost
importance that MIL algorithms can identify small tumors, even when they are
less than 1% of the size of the WSI. Existing studies have attempted to address
this issue using attention-based architectures and instance selection-based
methodologies, but have not yielded significant improvements. This paper
proposes cross-attention-based salient instance inference MIL (CASiiMIL), which
involves a novel saliency-informed attention mechanism, to identify breast
cancer lymph node micro-metastasis on WSIs without the need for any
annotations. Apart from this new attention mechanism, we introduce a negative
representation learning algorithm to facilitate the learning of
saliency-informed attention weights for improved sensitivity on tumor WSIs. The
proposed model outperforms the state-of-the-art MIL methods on two popular
tumor metastasis detection datasets, and demonstrates great cross-center
generalizability. In addition, it exhibits excellent accuracy in classifying
WSIs with small tumor lesions. Moreover, we show that the proposed model has
excellent interpretability attributed to the saliency-informed attention
weights. We strongly believe that the proposed method will pave the way for
training algorithms for early tumor detection on large datasets where acquiring
fine-grained annotations is practically impossible.

该研究提出了一种基于交叉关注的突出实例推断多实例学习方法，通过一种新的显著性提醒的关注机制，在 WSIs 上无需任何标注即可识别乳腺癌淋巴结微转移，并展示出对小肿瘤病变 WSIs 的出色准确性，具有良好的解释性特点。