Negative Pseudo Labeling using Class Proportion for Semantic Segmentation in Pathology

no code implementations • ECCV 2020 • Hiroki Tokunaga, Brian Kenji Iwana, Yuki Teramoto, Akihiko Yoshizawa, Ryoma Bise

We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i. e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining.

Cell Detection Cell Tracking +1

Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology

no code implementations • CVPR 2019 • Hiroki Tokunaga, Yuki Teramoto, Akihiko Yoshizawa, Ryoma Bise

A key assumption is that the importance of the magnifications depends on the characteristics of the input image, such as cancer subtypes.

Image Segmentation Segmentation +2