Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research

• URL: http://arxiv.org/abs/2306.15651v1
• Date: Tue, 27 Jun 2023 17:47:12 GMT
• Title: Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research
• Authors: Tanjida Kabir, Luyao Chen, Muhammad F Walji, Luca Giancardo, Xiaoqian Jiang, Shayan Shams
• Abstract summary: The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, retrieves the best-matched images based on the text query. Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82.
• Score: 6.628588447133907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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