ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
Table 3: AUCs of ROC curves for multi-label classification in different DCNN model setting.
['[BOLD] Setting', '[BOLD] Atelectasis', '[BOLD] Cardiomegaly', '[BOLD] Effusion', '[BOLD] Infiltration', '[BOLD] Mass', '[BOLD] Nodule', '[BOLD] Pneumonia', '[BOLD] Pneumothorax']
Multi-label Disease Classification: Fig. The quantitative performance varies greatly, in which the model based on ResNet-50 achieves the best results. The “Cardiomegaly” (AUC=0.8141) and “Pneumothorax” (AUC=0.7891) classes are consistently well-recognized compared to other groups while the detection ratios can be relatively lower for pathologies which contain small objects, e.g., “Mass” (AUC=0.5609) and “Nodule” classes. Mass is difficult to detect due to its huge within-class appearance variation. The lower performance on “Pneumonia” (AUC=0.6333) is probably because of lack of total instances in our patient population (less than 1% X-rays labeled as Pneumonia). Last, we demonstrate the performance improvement by using the positive/negative instances balanced loss functions e.g. AUC for “Cardiomegaly” is increased from 0.7262 to 0.8141 and from 0.5164 to 0.6333 for “Pneumonia”.