ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
Table 4: Pathology localization accuracy and average false positive number for 8 disease classes.
['[BOLD] T(IoBB)', '[BOLD] Atelectasis', '[BOLD] Cardiomegaly', '[BOLD] Effusion', '[BOLD] Infiltration', '[BOLD] Mass', '[BOLD] Nodule', '[BOLD] Pneumonia', '[BOLD] Pneumothorax']
Disease Localization: Leveraging the fine-tuned DCNN models for multi-label disease classification, we can calculate the disease heatmaps using the activations of the transition layer and the weights from the prediction layer, and even generate the B-Boxes for each pathology candidate. The computed bounding boxes are evaluated against the hand annotated ground truth (GT) boxes (included in ChestX-ray8). Although the total number of B-Box annotations (1,600 instances) is relatively small compared to the entire dataset, it may be still sufficient to get a reasonable estimate on how the proposed framework performs on the weakly-supervised disease localization task. To examine the accuracy of computerized B-Boxes versus the GT B-Boxes, two types of measurement are used, i.e, the standard Intersection over Union ratio (IoU) or the Intersection over the detected B-Box area ratio (IoBB) (similar to Area of Precision or Purity). Due to the relatively low spatial resolution of heatmaps (32×32) in contrast to the original image dimensions (1024×1024), the computed B-Boxes are often larger than the according GT B-Boxes. Therefore, we define a correct localization by requiring either IoU>T(IoU) or IoBB>T(IoBB). Refer to the supplementary material for localization performance under varying T(IoU). Please refer to the supplementary material for qualitative exemplary disease localization results for each of 8 pathology classes.