Diameter-based pseudo labeling for pathological image segmentation

Yuki Shigeyasu, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Ryoma Bise

Abstract: This paper proposes a semi-and-weak supervised pathological image segmentation method that effectively leverages the pre-recorded long-diameter information of a tumor in clinical as weak supervision. By leveraging the tumor diameter, the proposed method can accurately identify candidate tumor regions for pseudo-label selection. The accurate pseudo labels can improve the segmentation performance. The experimental results demonstrate the effectiveness of our method, which achieved the best performance among the comparative methods.