Breast cancer is one of the most challenging and concerning topics in deep learning applications due to its uncertain anomalies and insufficient quality of medical imaging. To address the problem, in this study, a unique attention mechanism is introduced that utilizes the shallow-scale information for generating spatial attention vectors and applying to the deep-scale feature through the transformation by bilinear algorithm to ensure the shape of attention vector with its targeting applying feature. Unlike other conventional attention mechanisms, which generate and apply attention weights based on specific feature levels, this introduced method is different as it creates attention weights using shallow-level features but then applies the weights for other deep-level information. This proposed mechanism is called the Spatial Shallow Feature Attention Module (SSFAM) and is designed for plug-and-play purposes. It is further integrated into the YOLOv11 architecture. As a result, the introduced method can greatly boost the capability of YOLOv11 in breast cancer instance segmentation compared to other models. In detail, the combination of SSFAM and YOLOv11 achieves up to 69.85% recall, 65.56% mAP50, and 40.20% mAP50-95 in the BUSI test set while exceptionally adding little parameter complexity. These results suggest that this mechanism could be further developed to address various challenges in deep learning architectures for medical imaging.