# TorchXRayVision: A library of chest X-ray datasets and models

arXiv: https://arxiv.org/abs/2111.00595 · HTML: https://mlsanity.com/paper/2111.00595/

## Key facts

- Cited by 131 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [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)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [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)
- [COVID-19 Image Data Collection: Prospective Predictions Are the Future](https://mlsanity.com/paper/2006.11988/) (2020)
- [On the limits of cross-domain generalization in automated X-ray prediction](https://mlsanity.com/paper/2002.02497/) (2020)
- [Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling](https://mlsanity.com/paper/2005.14480/) (2020)
- [Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning](https://mlsanity.com/paper/2005.11856/) (2020)
- [GAN-based Data Augmentation for Chest X-ray Classification](https://mlsanity.com/paper/2107.02970/) (2021)
- [Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System](https://mlsanity.com/paper/1901.11210/) (2019)
- [Confounder-Aware Visualization of ConvNets](https://mlsanity.com/paper/1907.12727/) (2019)
- [Potential Features of ICU Admission in X-ray Images of COVID-19 Patients](https://mlsanity.com/paper/2009.12597/) (2020)
- [Predicting intubation support requirement of patients using Chest X-ray with Deep Representation Learning](https://mlsanity.com/paper/2011.01787/) (2020)
- [MAVIDH Score: A COVID-19 Severity Scoring using Chest X-Ray Pathology Features](https://mlsanity.com/paper/2011.14983/) (2020)

## Built on by (most cited first, 30 of 131)

- [RoentGen: Vision-Language Foundation Model for Chest X-ray Generation](https://mlsanity.com/paper/2211.12737/) (2022)
- [Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains](https://mlsanity.com/paper/2210.04133/) (2022)
- [Deep Model Reassembly](https://mlsanity.com/paper/2210.17409/) (2022)
- [Integrated multimodal artificial intelligence framework for healthcare applications](https://mlsanity.com/paper/2202.12998/) (2022)
- [MedRAX: Medical Reasoning Agent for Chest X-ray](https://mlsanity.com/paper/2502.02673/) (2025)
- [Knowledge Matters: Radiology Report Generation with General and Specific Knowledge](https://mlsanity.com/paper/2112.15009/) (2021)
- [FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion](https://mlsanity.com/paper/2402.03226/) (2024)
- [KiUT: Knowledge-injected U-Transformer for Radiology Report Generation](https://mlsanity.com/paper/2306.11345/) (2023)
- [LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation](https://mlsanity.com/paper/2305.11490/) (2023)
- [Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity](https://mlsanity.com/paper/2305.08252/) (2023)
- [VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge](https://mlsanity.com/paper/2411.12915/) (2024)
- [Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI](https://mlsanity.com/paper/2501.09731/) (2025)
- [Explaining Chest X-ray Pathologies in Natural Language](https://mlsanity.com/paper/2207.04343/) (2022)
- [Medical Image Captioning via Generative Pretrained Transformers](https://mlsanity.com/paper/2209.13983/) (2022)
- [CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays](https://mlsanity.com/paper/2208.03873/) (2022)
- [Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models](https://mlsanity.com/paper/2306.01322/) (2023)
- [Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research](https://mlsanity.com/paper/2311.09402/) (2023)
- [Multimodal Explainability via Latent Shift applied to COVID-19 stratification](https://mlsanity.com/paper/2212.14084/) (2022)
- [Chest-Diffusion: A Light-Weight Text-to-Image Model for Report-to-CXR Generation](https://mlsanity.com/paper/2407.00752/) (2024)
- [Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification](https://mlsanity.com/paper/2203.09860/) (2022)
- [Vision-Language Generative Model for View-Specific Chest X-ray Generation](https://mlsanity.com/paper/2302.12172/) (2023)
- [Stability Guarantees for Feature Attributions with Multiplicative Smoothing](https://mlsanity.com/paper/2307.05902/) (2023)
- [A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis](https://mlsanity.com/paper/2405.14839/) (2024)
- [DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis](https://mlsanity.com/paper/2211.13229/) (2022)
- [FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models](https://mlsanity.com/paper/2411.18672/) (2024)
- [CXR-Agent: Vision-language models for chest X-ray interpretation with uncertainty aware radiology reporting](https://mlsanity.com/paper/2407.08811/) (2024)
- [Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning](https://mlsanity.com/paper/2403.06516/) (2024)
- [BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification](https://mlsanity.com/paper/2203.01937/) (2022)
- [Unsupervised pre-training of graph transformers on patient population graphs](https://mlsanity.com/paper/2207.10603/) (2022)
- [Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data](https://mlsanity.com/paper/2402.01054/) (2024)

## Abstract (arXiv)

TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. It provides a common interface and common pre-processing chain for a wide set of publicly available chest X-ray datasets. In addition, a number of classification and representation learning models with different architectures, trained on different data combinations, are available through the library to serve as baselines or feature extractors.