Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research
This provides a convenient search tool for dental researchers to access diagnostic features from radiographs, addressing the lack of annotated data and expert tools in this domain.
The paper tackled the problem of searching dental radiographs using text queries by developing Dental CLAIRES, a contrastive learning-based retrieval tool, which achieved a hit@3 ratio of 96% and an MRR of 0.82.
Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, utilizes periapical radiographs and associated clinical details such as periodontal diagnosis, demographic information to retrieve the best-matched images based on the text query. We applied a contrastive representation learning method to find images described by the user's text by maximizing the similarity score of positive pairs (true pairs) and minimizing the score of negative pairs (random pairs). Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We also designed a graphical user interface that allows researchers to verify the model's performance with interactions.