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.

External IDs:dblp:conf/embc/ShigeyasuHYTB24