Fig. 1

Overview of methodology. The teeth in the input PR (a) are segmented by Mask DINO and their FDI numbers are predicted(b) [25]. For each predicted tooth, a cropped image is made with the segmentation as extra channel (c, ROI extraction). This image is processed by four binary classifiers, one for each diagnosis, whose predictions are aggregated using an MLP [34], and by a multi-label classifier who returns multiple predictions via its CSRA heads [40]. All multi-label predictions are summed and these final scores are used to add diagnoses to the tooth segmentations (d). Non-diagnosed teeth are predicted, but not shown in the result for clarity. The label is the tooth’s FDI number with C = early caries, D = deep caries, P = periapical lesion, I = impacted