Multi-module UNet++ for colon cancer histopathological image segmentation.

In the pathological diagnosis of colorectal cancer, the precise segmentation of glandular and cellular contours serves as the fundamental basis for achieving accurate clinical diagnosis. However, this task presents significant challenges due to complex phenomena such as nuclear staining heterogeneity, variations in nuclear size, boundary overlap, and nuclear clustering. With the continuous advancement of deep learning techniques-particularly encoder-decoder architectures-and the emergence of various high-performance functional modules, multi module collaborative fusion has become an effective approach to enhance segmentation performance. To this end, this study proposes the RPAU-Net++ model, which integrates the ResNet-50 encoder (R), the Joint Pyramid Fusion Module (P), and the Convolutional Block Attention Module (A) into the UNet++ framework, forming a multi-module-enhanced segmentation architecture. Specifically, ResNet-50 mitigates gradient vanishing and degradation issues in deep network training through residual skip connections, thereby improving model convergence stability and feature representation depth. JPFM achieves progressive fusion of cross-layer features via a multi-scale feature pyramid, enhancing the encoding capability for complex tissue structures and fine boundary information. CBAM employs adaptive weight allocation in both spatial and channel dimensions to focus on target region features while effectively suppressing irrelevant background noise, thereby improving feature discriminability. Comparative experiments on the GlaS and CoNIC colorectal cancer pathology datasets, as well as the more challenging PanNuke dataset, demonstrate that RPAU-Net++ significantly outperforms mainstream models in key segmentation metrics such as IoU and Dice, providing a more accurate solution for pathological image segmentation in colorectal cancer.