Simple SummaryRecent advancements in AI have revolutionized cancer research, especially in the analysis of histopathological imaging data with minimal human involvement. Early detection of lymph node metastasis in breast cancer is vital for treatment outcomes. This paper introduces a novel approach that combines representation learning and deep learning (DL) to detect small tumors (STs) without neglecting larger ones. The proposed method uses representation learning to identify STs in histopathology images, followed by DL algorithms for breast cancer detection. Extensive evaluation shows remarkable accuracy in detecting STs without compromising larger-lesion detection. This approach enables early detection, timely intervention, and potentially improved treatment outcomes. The integration of representation learning and DL offers a promising solution for ST detection in breast cancer. By reducing human involvement and leveraging AI capabilities, the proposed method achieves impressive accuracy in identifying STs. Further research and validation could enhance diagnostic capabilities and personalized treatment strategies, ultimately benefiting breast cancer patients.AbstractThe early diagnosis of lymph node metastasis in breast cancer is essential for enhancing treatment outcomes and overall prognosis. Unfortunately, pathologists often fail to identify small or subtle metastatic deposits, leading them to rely on cytokeratin stains for improved detection, although this approach is not without its flaws. To address the need for early detection, multiple-instance learning (MIL) has emerged as the preferred deep learning method for automatic tumor detection on whole slide images (WSIs). However, existing methods often fail to identify some small lesions due to insufficient attention to small regions. Attention-based multiple-instance learning (ABMIL)-based methods can be particularly problematic because they may focus too much on normal regions, leaving insufficient attention for small-tumor lesions. In this paper, we propose a new ABMIL-based model called normal representative keyset ABMIL (NRK-ABMIL), which addresseses this issue by adjusting the attention mechanism to give more attention to lesions. To accomplish this, the NRK-ABMIL creates an optimal keyset of normal patch embeddings called the normal representative keyset (NRK). The NRK roughly represents the underlying distribution of all normal patch embeddings and is used to modify the attention mechanism of the ABMIL. We evaluated NRK-ABMIL on the publicly available Camelyon16 and Camelyon17 datasets and found that it outperformed existing state-of-the-art methods in accurately identifying small tumor lesions that may spread over a few patches. Additionally, the NRK-ABMIL also performed exceptionally well in identifying medium/large tumor lesions.