PG-FANet

Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation (https://doi.org/10.1016/j.eswa.2023.122093) published in Expert Systems with Applications

by Qiangguo Jin, Hui Cui, Changming Sun, et al.

Example results

• Figure 1: The overall architecture of the proposed semi-supervised histopathology image segmentation model using two-stage PG-FANet and interand intra-uncertainty and consistency regularization. EMA denotes exponential moving average.

• Overview of our (a) PG-FANet with two-stage sub-networks, (b) mask-guided feature enhancement (MGFE) module, (c) multi-scale feature aggregation, and (d) multi-stage feature aggregation.

• Segmentation results on the MoNuSeg and CRAG datasets using our fully supervised PG-FANet with 100% labeled data and semisupervised learning with 5%, 10%, 20%, and 50% of the labeled data.

Dataset

CRAG,MoNuSeg

Citation

If the code is helpful for your research, please consider citing:

@article{jin2024inter,
title={Inter-and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation},
author={Jin, Qiangguo and Cui, Hui and Sun, Changming and Song, Yang and Zheng, Jiangbin and Cao, Leilei and Wei, Leyi and Su, Ran},
journal={Expert Systems with Applications},
volume={238},
pages={122093},
year={2024},
publisher={Elsevier}
}

@inproceedings{jin2022semi,
title={Semi-supervised histological image segmentation via hierarchical consistency enforcement},
author={Jin, Qiangguo and Cui, Hui and Sun, Changming and Zheng, Jiangbin and Wei, Leyi and Fang, Zhenyu and Meng, Zhaopeng and Su, Ran},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={3--13},
year={2022},
organization={Springer}
}

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