The segmentation of histopathological images is an important problem in
the field of medical image processing. However, the high cost of manual
annotation and the lack of large-scale annotated data are important
factors that restrict the application of deep learning methods in this
field. To overcome these challenges, we propose a two-stage weakly
supervised semantic segmentation model based on pathological tissue
relationships. Our framework leverages the potential relationships
between various tissues in histopathological images through a similar
Graph Parsing Attention Mechanism to improve segmentation performance.
At the segmentation stage, we validate the effectiveness of our cyclic
pseudo-mask strategy for denoising and segmentation, and further enhance
segmentation performance through multi-resolution supervision. Our model
exhibits advanced performance on both BCSS and LUAD histopathology
datasets, demonstrating the superiority of our framework. The
contribution of our paper lies in the introduction of prior knowledge
about the potential relationships between tissues into the weakly
supervised semantic segmentation domain, which realizes high-quality
histopathological image segmentation on small sample datasets. Moreover,
we propose novel strategies such as cyclic pseudo-masks and
multi-resolution supervision to improve segmentation performance. Our
framework has significant application value and theoretical
significance, providing accurate diagnostic support for doctors.