  This paper describes our solution for the Dental Enumeration and Diagnosis on
Panoramic X-rays Challenge at MICCAI 2023. Our approach consists of a
multi-step framework tailored to the task of detecting and classifying abnormal
teeth. The solution includes three sequential stages: dental instance
detection, healthy instance filtering, and abnormal instance classification. In
the first stage, we employed a Faster-RCNN model for detecting and identifying
teeth. In subsequent stages, we designed a model that merged the encoding
pathway of a pretrained U-net, optimized for dental lesion detection, with the
Vgg16 architecture. The resulting model was first used for filtering out
healthy teeth. Then, any identified abnormal teeth were categorized,
potentially falling into one or more of the following conditions: embedded,
periapical lesion, caries, deep caries. The model performing dental instance
detection achieved an AP score of 0.49. The model responsible for identifying
healthy teeth attained an F1 score of 0.71. Meanwhile, the model trained for
multi-label dental disease classification achieved an F1 score of 0.76. The
code is available at
https://github.com/tudordascalu/2d-teeth-detection-challenge.

Image and Video Processing;Computer Vision and Pattern Recognition