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Found 5 papers, 3 papers with code
VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs.
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.
Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels
The chest X-rays (CXRs) is one of the views most commonly ordered by radiologists (NHS), which is critical for diagnosis of many different thoracic diseases.
A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans
We validate the method on the recent RSNA Intracranial Hemorrhage Detection challenge and on the CQ500 dataset.
Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels
The performance is on average better than 2. 6 out of 3 other individual radiologists with a mean AUC of 0. 930, which ranks first on the CheXpert leaderboard at the time of writing this paper.
Ranked #2 on
Multi-Label Classification
on CheXpert
