Weakly Supervised Nuclei Segmentation via Instance Learning

Model Overview

Abstract

Weakly supervised nuclei segmentation is a critical problem for pathological image analysis and greatly benefits the community due to the significant reduction of labeling cost. Adopting point annotations, previous methods mostly rely on less expressive representations for nuclei instances and thus have difficulty in handling crowded nuclei. In this paper, we propose to decouple weakly supervised semantic and instance segmentation in order to enable more effective subtask learning and to promote instance-aware representation learning. To achieve this, we design a modular deep network with two branches. a semantic proposal network and an instance encoding network, which are trained in a two-stage manner with an instance-sensitive loss. Empirical results show that our approach achieves the state-of-the-art performance on two public benchmarks of pathological images from different types of organs.

Publication
In IEEE International Symposium on Biomedical Imaging (ISBI), 2022
Qian He
Qian He
PhD Student

My research interests include single-view 3D reconstruction, 3D object representation, medical image segmentation and weak/semi-supervised learning.

Xuming He
Xuming He
Associate Professor

My research interests include few/low-shot learning, graph neural networks and video understanding.