AL-Net: Attention Learning Network Based on Multi-Task Learning for Cervical Nucleus Segmentation

Cervical nucleus segmentation is a crucial and challenging issue in automatic pathological diagnosis due to uneven staining, blurry boundaries, and adherent or overlapping nuclei in nucleus images. To overcome the limitation of current methods, we propose a multi-task network based on U-Net for cerv...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2022-06, Vol.26 (6), p.2693-2702
Main Authors: Zhao, Jing, He, Yong-Jun, Zhao, Si-Qi, Huang, Jin-Jie, Zuo, Wang-Meng
Format: Article
Language:English
Subjects:
Attention
attention learning
Boundaries
Cell Nucleus
Cervix Uteri
Codec
Context
context encoding
Convolution
Datasets
Encoding
Feature extraction
Female
Humans
Image Processing, Computer-Assisted - methods
Image segmentation
Learning
Modules
multi-task network
Multitasking
Neural Networks, Computer
Nuclei
Nucleus segmentation
Shape
shape loss
Task analysis
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Summary:Cervical nucleus segmentation is a crucial and challenging issue in automatic pathological diagnosis due to uneven staining, blurry boundaries, and adherent or overlapping nuclei in nucleus images. To overcome the limitation of current methods, we propose a multi-task network based on U-Net for cervical nucleus segmentation. This network consists of a primary task and an auxiliary task. The primary task is employed to predict nuclei regions. The auxiliary task, which predicts the boundaries of nuclei, is designed to improve the feature extraction of the main task. Furthermore, a context encoding layer is added behind each encoding layer of the U-Net. The output of each context encoding layer is processed by an attention learning module and then fused with the features of the decoding layer. In addition, a codec block is used in the attention learning module to obtain saliency-based attention and focused attention simultaneously. Experiment results show that the proposed network performs better than the state-of-the-art methods on the 2014 ISBI dataset, BNS, MoNuSeg, and our nucluesSeg dataset.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2021.3136568