# Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation

arXiv: https://arxiv.org/abs/2106.01860 · HTML: https://mlsanity.com/paper/2106.01860/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [DUNet: A deformable network for retinal vessel segmentation](https://mlsanity.com/paper/1811.01206/) (2018)
- [A review: Deep learning for medical image segmentation using multi-modality fusion](https://mlsanity.com/paper/2004.10664/) (2020)
- [Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model](https://mlsanity.com/paper/1903.01248/) (2019)
- [Double-Uncertainty Weighted Method for Semi-supervised Learning](https://mlsanity.com/paper/2010.09298/) (2020)
- [3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes](https://mlsanity.com/paper/1607.00582/) (2016)
- [Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation](https://mlsanity.com/paper/1907.11835/) (2019)

## Built on by (most cited first, 14 of 14)

- [Source-Free Unsupervised Domain Adaptation: A Survey](https://mlsanity.com/paper/2301.00265/) (2022)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.13930/) (2021)
- [Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation](https://mlsanity.com/paper/2211.14491/) (2022)
- [Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels](https://mlsanity.com/paper/2206.07994/) (2022)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks](https://mlsanity.com/paper/2210.09919/) (2022)
- [Deep Learning in Single-Cell Analysis](https://mlsanity.com/paper/2210.12385/) (2022)
- [HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss](https://mlsanity.com/paper/2504.07827/) (2025)
- [Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels](https://mlsanity.com/paper/2409.05024/) (2024)
- [Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification](https://mlsanity.com/paper/2207.07303/) (2022)
- [Cross-Validation Is All You Need: A Statistical Approach To Label Noise Estimation](https://mlsanity.com/paper/2306.13990/) (2023)
- [Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware Learning](https://mlsanity.com/paper/2311.18496/) (2023)
- [Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications](https://mlsanity.com/paper/2510.03318/) (2025)

## Abstract (arXiv)

Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morphology of vessels, resulting in the extreme lack of high-quality labeled data. Without sufficient high-quality annotations, the usual data-driven learning-based approaches struggle with deficient training. On the other hand, directly introducing additional data with low-quality annotations may confuse the network, leading to undesirable performance degradation. To address this issue, we propose a novel mean-teacher-assisted confident learning framework to robustly exploit the noisy labeled data for the challenging hepatic vessel segmentation task. Specifically, with the adapted confident learning assisted by a third party, i.e., the weight-averaged teacher model, the noisy labels in the additional low-quality dataset can be transformed from "encumbrance" to "treasure" via progressive pixel-wise soft-correction, thus providing productive guidance. Extensive experiments using two public datasets demonstrate the superiority of the proposed framework as well as the effectiveness of each component.