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Found 4 papers, 4 papers with code
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
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.
Ranked #95 on
Multi-Label Classification
on CheXpert
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries.
Ranked #1 on
Multi-Label Classification
on MRNet
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
To evaluate models robustly and to get an estimate of radiologist performance, we collect additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal studies.
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists.
Ranked #3 on
Pneumonia Detection
on ChestX-ray14
