CheXpert CXR
CheXpert is a large dataset of 224,316 chest radiographs from 65,240 patients for automated chest X-ray interpretation. The dataset includes uncertainty labels for 14 medical observations extracted from radiology reports. It serves as a benchmark for developing and evaluating automated chest radiograph interpretation models.
MedGemma 4B IT from Google currently leads the CheXpert CXR leaderboard with a score of 0.481 across 1 evaluated AI models.
What CheXpert CXR measures
CheXpert CXR is a image benchmark that evaluates large language models on healthcare and vision tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.5, with the leader reaching 0.5.
Compare leaders on the best AI for healthcare and best AI for vision leaderboards.
Publication
- Paper
- CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
- Authors
- Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, and 16 others
- Published
- arXiv
- 1901.07031
Abstract
Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. The dataset is freely available at https://stanfordmlgroup.github.io/competitions/chexpert .
MedGemma 4B IT leads with 48.1%.
Progress Over Time
Interactive timeline showing model performance evolution on CheXpert CXR
CheXpert CXR Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Google | 4B | — | — |
FAQ
Common questions about CheXpert CXR.
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