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Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese
Our best model (CheXpert-pretrained EfficientNet-B2) yields an F1-score of 0. 6989 (95% CI 0. 6740, 0. 7240), AUC of 0. 7912, sensitivity of 0. 7064 and specificity of 0. 8760 for the abnormal diagnosis in general.
Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling
This work aims at developing and validating a precise, fast multi-phase classifier to recognize three main types of contrast phases in abdominal CT scans.
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.
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network
Brain tumor segmentation plays an essential role in medical image analysis.
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