Weakly-Supervised pathological image segmentation for oral squamous cell carcinoma
Considering the high resolution of Oral Squamous Cell Carcinoma(OSCC)pathological image,we propose to simply draw curves to cover up all foreground regions,generating coarse labels with back-ground around involved in.The overall time of coarse annotation is about one sixth the time of professional precise annotation.Considering the fact that background labels are clean and the foreground labels are noisy,multiple background regions are used to establish templates to filter out the mislabelled foreground pixels and refine the foreground segments.Specifically,Background Similarity-aware Mean Teacher(BS-MT)is advanced,which explores background representations with high diversity and robustness and guides the student model to generate high-quality foreground labels via background pixel filtering.On the privately-es-tablished OSCC-CA dataset,our method significantly outperforms U-Net by 2.5％and other widely-used weakly-supervised methods by more than 1.1％in terms of mIoU.
weakly-supervised segmentationpathological image segmentationbackground similarity-a-ware mean teacher
万方数据
维普
