# Meta Mask Correction for Nuclei Segmentation in Histopathological Image

arXiv: https://arxiv.org/abs/2111.12498 · HTML: https://mlsanity.com/paper/2111.12498/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Meta Label Correction for Noisy Label Learning](https://mlsanity.com/paper/1911.03809/) (2019)
- [Learning to Segment Skin Lesions from Noisy Annotations](https://mlsanity.com/paper/1906.03815/) (2019)
- [Boundary-assisted Region Proposal Networks for Nucleus Segmentation](https://mlsanity.com/paper/2006.02695/) (2020)
- [Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images](https://mlsanity.com/paper/2002.01020/) (2020)
- [Meta Corrupted Pixels Mining for Medical Image Segmentation](https://mlsanity.com/paper/2007.03538/) (2020)
- [Effects of annotation granularity in deep learning models for histopathological images](https://mlsanity.com/paper/2001.04663/) (2020)
- [A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning](https://mlsanity.com/paper/2110.13652/) (2021)

## Built on by (most cited first, 1 of 1)

- [Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware Learning](https://mlsanity.com/paper/2311.18496/) (2023)

## Abstract (arXiv)

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.