Sharper insights: Discrimination-enhanced semantic segmentation of similar morphology nuclei in breast cancer pathology images

Similar Morphology Cells (SMCs) play a critical role in pathology diagnosis. This study identifies and defines two primary challenges for SMCs segmentation based on detailed investigation. At the macro level of slides, the sample distribution within pathology images is highly imbalanced due to varying cell functionalities. At the micro level of cells, differences between SMCs are imperceptible. These two challenges lead to confusion between visually similar cell classes in SMCs segmentation, reducing segmentation accuracy. To address these issues, we propose Sharper Insights (SI), a novel approach designed to enhance the model’s discriminative capabilities. Specifically, we introduce a Discrimination Enhancement Module (DEM) to increase the model’s sensitivity to subtle variations among SMCs. This module utilizes two attention mechanisms to sharpen the model’s focus on minute differences. Additionally, a novel loss function, Minority Categories Augmentation (MCA), is introduced to mitigate distribution imbalance by dynamically adjusting category-specific weights. Finally, to verify the effectiveness and generalization of the proposed method SI, we curate a comprehensive breast cancer dataset SimMorphBC. It primarily focuses on SMCs, which have not been explicitly proposed in previous datasets. Extensive experiments on breast cancer pathology images demonstrate the superior performance of the proposed approach.