  Tissue-level semantic segmentation is a vital step in computational
pathology. Fully-supervised models have already achieved outstanding
performance with dense pixel-level annotations. However, drawing such labels on
the giga-pixel whole slide images is extremely expensive and time-consuming. In
this paper, we use only patch-level classification labels to achieve tissue
semantic segmentation on histopathology images, finally reducing the annotation
efforts. We proposed a two-step model including a classification and a
segmentation phases. In the classification phase, we proposed a CAM-based model
to generate pseudo masks by patch-level labels. In the segmentation phase, we
achieved tissue semantic segmentation by our proposed Multi-Layer
Pseudo-Supervision. Several technical novelties have been proposed to reduce
the information gap between pixel-level and patch-level annotations. As a part
of this paper, we introduced a new weakly-supervised semantic segmentation
(WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). We conducted several
experiments to evaluate our proposed model on two datasets. Our proposed model
outperforms two state-of-the-art WSSS approaches. Note that we can achieve
comparable quantitative and qualitative results with the fully-supervised
model, with only around a 2\% gap for MIoU and FwIoU. By comparing with manual
labeling, our model can greatly save the annotation time from hours to minutes.
The source code is available at: \url{https://github.com/ChuHan89/WSSS-Tissue}.
