Multiplex aggregation combining sample reweight composite network for pathology image segmentation

In digital pathology, nuclei segmentation is a critical task for pathological image analysis, holding significant importance for diagnosis and research. However, challenges such as blurred boundaries between nuclei and background regions, domain shifts between pathological images, and uneven distribution of nuclei pose significant obstacles to segmentation tasks. To address these issues, we propose an innovative Causal inference inspired Diversified aggregation convolution Network named CDNet, which integrates a Diversified Aggregation Convolution (DAC), a Causal Inference Module (CIM) based on causal discovery principles, and a comprehensive loss function. DAC improves the issue of unclear boundaries between nuclei and background regions, and CIM enhances the model's cross-domain generalization ability. A novel Stable-Weighted Combined loss function was designed that combined the chunk-computed Dice Loss with the Focal Loss and the Causal Inference Loss to address the issue of uneven nuclei distribution. Experimental evaluations on the MoNuSeg, GLySAC, and MoNuSAC datasets demonstrate that CDNet significantly outperforms other models and exhibits strong generalization capabilities. Specifically, CDNet outperforms the second-best model by 0.79% (mIoU) and 1.32% (DSC) on the MoNuSeg dataset, by 2.65% (mIoU) and 2.13% (DSC) on the GLySAC dataset, and by 1.54% (mIoU) and 1.10% (DSC) on the MoNuSAC dataset. Code is publicly available at https://github.com/7FFDW/CDNet.