A pixel affinity adaptive expansion weakly supervised skin image segmentation method

Abstract

Image annotation of medical scenes is expensive, then weak supervision methods have emerged. Therefore, this paper proposes a pixel affinity adaptive expansion model for medical image label generation in skin scenes. The method is divided into two stages. In the first stage, initial pseudo label is generated based on the characteristics of the skin image using the proposed self-adaptation expansion module . In the second stage, the generated initial pseudo label uses the characteristics of the pixel pair to distinguish the foreground background and class boundaries to generate the final label. Finally, the generated tags are used for fully supervised segmentation. Experiments were conducted on the ISIC 2016 and ISIC 2017 datasets, respectively. The MIoU of the proposed method was 63.24% and 50.43% on the two datasets, while the Dice-score was 76.13% and 67.79%, the accuracy rate was 81.28% and 64.79%, and the recall rate was 73.54% and 71.03%, which is higher than most existing weakly supervised semantic segmentation algorithms, and achieved Sota (State of the arts) on the two skin datasets.