![]() This is the highest AUC score yet reported to date. Our model is trained on over 200,000 CXRs of the recently released CheXpert dataset and achieves a mean area under the curve (AUC) of 0.940 in predicting 5 selected pathologies from the validation set. We also propose to use the label smoothing technique for a better handling of uncertain samples, which occupy a significant portion of almost every CXR dataset. We tackle this problem by training state-of-the-art CNNs that exploit hierarchical dependencies among abnormality labels. This paper presents a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) for predicting the presence of 14 common thoracic diseases and observations. However, accurately detecting the presence of multiple diseases from chest X-rays (CXRs) is still a challenging task. Specialized algorithms have been developed to detect several specific pathologies such as lung nodules or lung cancer. Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases.
0 Comments
Leave a Reply. |