Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images

• URL: http://arxiv.org/abs/2210.07862v1
• Date: Fri, 14 Oct 2022 14:34:26 GMT
• Title: Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images
• Authors: Pingyi Chen, Chenglu Zhu, Zhongyi Shui, Jiatong Cai, Sunyi Zheng, Shichuan Zhang, Lin Yang
• Abstract summary: We propose a self-supervised learning based approach with a Prior Self-activation Module (PSM) PSM generates self-activation maps from the input images to avoid labeling costs and further produce pseudo masks for the downstream task. Compared with other fully-supervised and weakly-supervised methods, our method can achieve competitive performance without any manual annotations.
• Score: 5.3882963853819845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of supervised deep learning models in medical image segmentation relies on detailed annotations. However, labor-intensive manual labeling is costly and inefficient, especially in dense object segmentation. To this end, we propose a self-supervised learning based approach with a Prior Self-activation Module (PSM) that generates self-activation maps from the input images to avoid labeling costs and further produce pseudo masks for the downstream task. To be specific, we firstly train a neural network using self-supervised learning and utilize the gradient information in the shallow layers of the network to generate self-activation maps. Afterwards, a semantic-guided generator is then introduced as a pipeline to transform visual representations from PSM to pixel-level semantic pseudo masks for downstream tasks. Furthermore, a two-stage training module, consisting of a nuclei detection network and a nuclei segmentation network, is adopted to achieve the final segmentation. Experimental results show the effectiveness on two public pathological datasets. Compared with other fully-supervised and weakly-supervised methods, our method can achieve competitive performance without any manual annotations.

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