Meta Mask Correction for Nuclei Segmentation in Histopathological Image
- URL: http://arxiv.org/abs/2111.12498v1
- Date: Wed, 24 Nov 2021 13:53:35 GMT
- Title: Meta Mask Correction for Nuclei Segmentation in Histopathological Image
- Authors: Jiangbo Shi, Chang Jia, Zeyu Gao, Tieliang Gong, Chunbao Wang, Chen Li
- Abstract summary: We propose a novel meta-learning-based nuclei segmentation method to leverage data with noisy masks.
Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data.
We show that our method achieves the state-of-the-art result.
- Score: 5.36728433027615
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Nuclei segmentation is a fundamental task in digital pathology analysis and
can be automated by deep learning-based methods. However, the development of
such an automated method requires a large amount of data with precisely
annotated masks which is hard to obtain. Training with weakly labeled data is a
popular solution for reducing the workload of annotation. In this paper, we
propose a novel meta-learning-based nuclei segmentation method which follows
the label correction paradigm to leverage data with noisy masks. Specifically,
we design a fully conventional meta-model that can correct noisy masks using a
small amount of clean meta-data. Then the corrected masks can be used to
supervise the training of the segmentation model. Meanwhile, a bi-level
optimization method is adopted to alternately update the parameters of the main
segmentation model and the meta-model in an end-to-end way. Extensive
experimental results on two nuclear segmentation datasets show that our method
achieves the state-of-the-art result. It even achieves comparable performance
with the model training on supervised data in some noisy settings.
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