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Found 6 papers, 3 papers with code
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis
As a result, the labeled data may contain a variety of human biases with a high rate of disagreement among annotators, which significantly affect the performance of supervised machine learning algorithms.
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks
Chest radiograph (CXR) interpretation in pediatric patients is error-prone and requires a high level of understanding of radiologic expertise.
VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays
We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans.
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network
Brain tumor segmentation plays an essential role in medical image analysis.
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.
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
