# Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation

arXiv: https://arxiv.org/abs/1907.11835 · HTML: https://mlsanity.com/paper/1907.11835/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2019.

## Built on by (most cited first, 25 of 25)

- [Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation](https://mlsanity.com/paper/2107.10100/) (2021)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training](https://mlsanity.com/paper/2205.04723/) (2022)
- [Learning to Segment from Noisy Annotations: A Spatial Correction Approach](https://mlsanity.com/paper/2308.02498/) (2023)
- [Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation](https://mlsanity.com/paper/2106.01860/) (2021)
- [FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise](https://mlsanity.com/paper/2312.12838/) (2023)
- [Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation](https://mlsanity.com/paper/2104.01975/) (2021)
- [Distilling effective supervision for robust medical image segmentation with noisy labels](https://mlsanity.com/paper/2106.11099/) (2021)
- [Weakly Supervised Few-Shot Segmentation Via Meta-Learning](https://mlsanity.com/paper/2109.01693/) (2021)
- [Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation](https://mlsanity.com/paper/2404.14852/) (2024)
- [On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice](https://mlsanity.com/paper/2206.06484/) (2022)
- [Robust Point Cloud Segmentation with Noisy Annotations](https://mlsanity.com/paper/2212.03242/) (2022)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training](https://mlsanity.com/paper/2308.16376/) (2023)
- [Co-Seg: An Image Segmentation Framework Against Label Corruption](https://mlsanity.com/paper/2102.00523/) (2021)
- [Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels](https://mlsanity.com/paper/2409.05024/) (2024)
- [Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate](https://mlsanity.com/paper/2112.02781/) (2021)
- [MSE-Nets: Multi-annotated Semi-supervised Ensemble Networks for Improving Segmentation of Medical Image with Ambiguous Boundaries](https://mlsanity.com/paper/2311.10380/) (2023)
- [Points2Polygons: Context-Based Segmentation from Weak Labels Using Adversarial Networks](https://mlsanity.com/paper/2106.02804/) (2021)
- [Learning Trustworthy Model from Noisy Labels based on Rough Set for Surface Defect Detection](https://mlsanity.com/paper/2301.10441/) (2023)
- [Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware Learning](https://mlsanity.com/paper/2311.18496/) (2023)
- [Beyond Strong labels: Weakly-supervised Learning Based on Gaussian Pseudo Labels for The Segmentation of Ellipse-like Vascular Structures in Non-contrast CTs](https://mlsanity.com/paper/2402.03492/) (2024)
- [Learning from Sparse Point Labels for Dense Carcinosis Localization in Advanced Ovarian Cancer Assessment](https://mlsanity.com/paper/2507.06643/) (2025)
- [Generalizing Abstention for Noise-Robust Learning in Medical Image Segmentation](https://mlsanity.com/paper/2601.14039/) (2026)
- [Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels](https://mlsanity.com/paper/2607.10841/) (2026)

## Abstract (arXiv)

Deep learning methods have achieved promising performance in many areas, but they are still struggling with noisy-labeled images during the training process. Considering that the annotation quality indispensably relies on great expertise, the problem is even more crucial in the medical image domain. How to eliminate the disturbance from noisy labels for segmentation tasks without further annotations is still a significant challenge. In this paper, we introduce our label quality evaluation strategy for deep neural networks automatically assessing the quality of each label, which is not explicitly provided, and training on clean-annotated ones. We propose a solution for network automatically evaluating the relative quality of the labels in the training set and using good ones to tune the network parameters. We also design an overfitting control module to let the network maximally learn from the precise annotations during the training process. Experiments on the public biomedical image segmentation dataset have proved the method outperforms baseline methods and retains both high accuracy and good generalization at different noise levels.