The code will be coming soon... Title: Weakly Supervised Histopathology Tissue Segmentation with Multi-scale Voting and Online Noise Suppression Strategy Abstract: The development of an AI-assisted tissue segmentation method of digital pathology images is critical for cancer diagnosis and prognosis. Excellent performance has been achieved with the current fully supervised segmentation approach, which relies on a huge number of annotated data. However, drawing dense pixel-level annotations on the giga-pixel whole slide image (WSI) is extremely time-consuming and labor-intensive. To this end, we propose a tissue segmentation method using only patch-level classification labels to reduce such annotation burden. We introduce a framework with two phases of classification and segmentation. In the classification phase, we propose a multi-scale voting method on the CAM-based model to obtain more stable pseudo masks. In the segmentation phase, an Online Noise Suppression Strategy (ONSS) is proposed to encourage the model to focus on more reliable signals in the pseudo mask rather than noisy signals. Extensive experiments on two weakly supervised pathology image tissue segmentation datasets LUAD-HistoSeg and BCSS-WSSS demonstrate our model outperforms state-of-the-art WSSS methods using patch-level labels. Furthermore, our method exhibits superior generalization ability compared to other models and demonstrates promising adaptation performance on unseen domains with only small amounts of data.