# CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images

arXiv: https://arxiv.org/abs/2307.03293 · HTML: https://mlsanity.com/paper/2307.03293/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations](https://mlsanity.com/paper/2012.15029/) (2020)
- [Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders](https://mlsanity.com/paper/2006.13791/) (2020)
- [Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth](https://mlsanity.com/paper/1702.03407/) (2017)
- [Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding](https://mlsanity.com/paper/2210.03416/) (2022)
- [Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling](https://mlsanity.com/paper/2306.03934/) (2023)
- [Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis](https://mlsanity.com/paper/2203.10977/) (2022)
- [Learning Deformable Registration of Medical Images with Anatomical Constraints](https://mlsanity.com/paper/2001.07183/) (2020)
- [Hybrid graph convolutional neural networks for landmark-based anatomical segmentation](https://mlsanity.com/paper/2106.09832/) (2021)
- [Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets](https://mlsanity.com/paper/1903.03445/) (2019)
- [How precise are performance estimates for typical medical image segmentation tasks?](https://mlsanity.com/paper/2210.14677/) (2022)
- [Multi-center anatomical segmentation with heterogeneous labels via landmark-based models](https://mlsanity.com/paper/2211.07395/) (2022)

## Built on by (most cited first, 30 of 33)

- [Exploring scalable medical image encoders beyond text supervision](https://mlsanity.com/paper/2401.10815/) (2024)
- [RadEdit: stress-testing biomedical vision models via diffusion image editing](https://mlsanity.com/paper/2312.12865/) (2023)
- [Learning A Multi-Task Transformer Via Unified And Customized Instruction Tuning For Chest Radiograph Interpretation](https://mlsanity.com/paper/2311.01092/) (2023)
- [CF-Seg: Counterfactuals meet Segmentation](https://mlsanity.com/paper/2506.16213/) (2025)
- [In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review](https://mlsanity.com/paper/2501.10727/) (2025)
- [Augmenting Chest X-ray Datasets with Non-Expert Annotations](https://mlsanity.com/paper/2309.02244/) (2023)
- [Mask of truth: model sensitivity to unexpected regions of medical images](https://mlsanity.com/paper/2412.04030/) (2024)
- [A unified multi-task framework enables interpretable chest radiograph analysis](https://mlsanity.com/paper/2606.03417/) (2026)
- [Instruction-Guided Lesion Segmentation for Chest X-rays with Automatically Generated Large-Scale Dataset](https://mlsanity.com/paper/2511.15186/) (2025)
- [Unsupervised Training of Neural Cellular Automata on Edge Devices](https://mlsanity.com/paper/2407.18114/) (2024)
- [ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality](https://mlsanity.com/paper/2503.04522/) (2025)
- [Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray Analysis](https://mlsanity.com/paper/2503.09860/) (2025)
- [Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation](https://mlsanity.com/paper/2511.08402/) (2025)
- [Enhancing Multi-task Learning Capability of Medical Generalist Foundation Model via Image-centric Multi-annotation Data](https://mlsanity.com/paper/2504.09967/) (2025)
- [CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images](https://mlsanity.com/paper/2512.10715/) (2025)
- [Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine](https://mlsanity.com/paper/2411.19447/) (2024)
- [Multivariate Planar Curves: A Statistical Framework for Shape Analysis in Images](https://mlsanity.com/paper/2508.11780/) (2025)
- [Feature Quality and Adaptability of Medical Foundation Models: A Comparative Evaluation for Radiographic Classification and Segmentation](https://mlsanity.com/paper/2511.09742/) (2025)
- [From Cold Start to Active Learning: Embedding-Based Scan Selection for Medical Image Segmentation](https://mlsanity.com/paper/2601.18532/) (2026)
- [Pixel-level Counterfactual Contrastive Learning for Medical Image Segmentation](https://mlsanity.com/paper/2603.17110/) (2026)
- [PneumoLLM: Harnessing the Power of Large Language Model for Pneumoconiosis Diagnosis](https://mlsanity.com/paper/2312.03490/) (2023)
- [Evaluating Vision Language Models (VLMs) for Radiology: A Comprehensive Analysis](https://mlsanity.com/paper/2504.16047/) (2025)
- [Anatomic Feature Fusion Model for Diagnosing Calcified Pulmonary Nodules on Chest X-Ray](https://mlsanity.com/paper/2508.12562/) (2025)
- [Radiology Report Generation with Layer-Wise Anatomical Attention](https://mlsanity.com/paper/2512.16841/) (2025)
- [The Invisible Gorilla Effect in Out-of-distribution Detection](https://mlsanity.com/paper/2602.20068/) (2026)
- [The Impact of Preprocessing Methods on Racial Encoding and Model Robustness in CXR Diagnosis](https://mlsanity.com/paper/2603.05157/) (2026)
- [LoFi: Location-Aware Fine-Grained Representation Learning for Chest X-ray](https://mlsanity.com/paper/2603.19451/) (2026)
- [Instruction-Free Tuning of Large Vision Language Models for Medical Instruction Following](https://mlsanity.com/paper/2603.19482/) (2026)
- [CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs](https://mlsanity.com/paper/2606.08420/) (2026)
- [Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models](https://mlsanity.com/paper/2608.00976/) (2026)

## Abstract (arXiv)

The development of successful artificial intelligence models for chest X-ray analysis relies on large, diverse datasets with high-quality annotations. While several databases of chest X-ray images have been released, most include disease diagnosis labels but lack detailed pixel-level anatomical segmentation labels. To address this gap, we introduce an extensive chest X-ray multi-center segmentation dataset with uniform and fine-grain anatomical annotations for images coming from five well-known publicly available databases: ChestX-ray8, Chexpert, MIMIC-CXR-JPG, Padchest, and VinDr-CXR, resulting in 657,566 segmentation masks. Our methodology utilizes the HybridGNet model to ensure consistent and high-quality segmentations across all datasets. Rigorous validation, including expert physician evaluation and automatic quality control, was conducted to validate the resulting masks. Additionally, we provide individualized quality indices per mask and an overall quality estimation per dataset. This dataset serves as a valuable resource for the broader scientific community, streamlining the development and assessment of innovative methodologies in chest X-ray analysis. The CheXmask dataset is publicly available at: https://physionet.org/content/chexmask-cxr-segmentation-data/