# Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation

arXiv: https://arxiv.org/abs/2104.01975 · HTML: https://mlsanity.com/paper/2104.01975/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 5 Apr 2021.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/1903.00348/) (2019)
- [Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification](https://mlsanity.com/paper/1901.07759/) (2019)
- [Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation](https://mlsanity.com/paper/1907.11835/) (2019)
- [Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation](https://mlsanity.com/paper/1803.01199/) (2018)

## Built on by (most cited first, 11 of 11)

- [MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow](https://mlsanity.com/paper/2503.18968/) (2025)
- [Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation](https://mlsanity.com/paper/2107.10100/) (2021)
- [Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training](https://mlsanity.com/paper/2205.04723/) (2022)
- [PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation](https://mlsanity.com/paper/2208.05669/) (2022)
- [Robust Point Cloud Segmentation with Noisy Annotations](https://mlsanity.com/paper/2212.03242/) (2022)
- [Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation](https://mlsanity.com/paper/2307.05898/) (2023)
- [Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images](https://mlsanity.com/paper/2311.13719/) (2023)
- [A Bayesian Approach to Segmentation with Noisy Labels via Spatially Correlated Distributions](https://mlsanity.com/paper/2504.14795/) (2025)
- [Weighing Features of Lung and Heart Regions for Thoracic Disease Classification](https://mlsanity.com/paper/2105.12430/) (2021)
- [Noise-aware few-shot learning through bi-directional multi-view prompt alignment](https://mlsanity.com/paper/2603.11617/) (2026)
- [Detecting and refurbishing ground truth errors during training of deep learning-based echocardiography segmentation models](https://mlsanity.com/paper/2604.12832/) (2026)

## Abstract (arXiv)

The superior performance of CNN on medical image analysis heavily depends on the annotation quality, such as the number of labeled image, the source of image, and the expert experience. The annotation requires great expertise and labour. To deal with the high inter-rater variability, the study of imperfect label has great significance in medical image segmentation tasks. In this paper, we present a novel cascaded robust learning framework for chest X-ray segmentation with imperfect annotation. Our model consists of three independent network, which can effectively learn useful information from the peer networks. The framework includes two stages. In the first stage, we select the clean annotated samples via a model committee setting, the networks are trained by minimizing a segmentation loss using the selected clean samples. In the second stage, we design a joint optimization framework with label correction to gradually correct the wrong annotation and improve the network performance. We conduct experiments on the public chest X-ray image datasets collected by Shenzhen Hospital. The results show that our methods could achieve a significant improvement on the accuracy in segmentation tasks compared to the previous methods.