Ongoing Projects

Explore un/self-supervised applications in GMDB

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Principal Investigator: Prof. Bairong Shen

Institution: Institutes for Systems Genetics, West China Hospital

Deep learning models rely heavily on data, but in this area, GMDB is the only database and faces the difficulty of insufficient data. Meanwhile, face recognition databases have a large amount of data, and the similarity between GMDB and face recognition databases is that they are both facial images. Therefore, we try to train the model on unlabeled face recognition images first to learn facial...

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A survey on facial feature extraction methods in a small data environment: the diagnosis of rare genetic syndromes

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Principal Investigator: Mr. Lex Dingemans

Institution: Radboudumc

We would like to include the pretrained version of GestaltMatcher-arc in our survey study. This would require us to have access to the weights of the model, as published in previous work (GestaltMatcher-Arc). Of course, we will not (re)share these with anyone else outside our research group. For now, we do not aim to (re)train the model or use transfer learning, but are purely interested in the...

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Incorporating GestaltMatcher Database to refine a deep learning-based point-of-care screening tool for genetic syndromes in children

Nov. 19th 2024

Principal Investigator: Dr. Xinyang Liu

Institution: Children's National Hospital

Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We have developed and evaluated a deep learning-based point-of-care screening tool for genetic syndromes in children. The tool analyzes an individual’s facial image to predict the likelihood of a syndromic condition. To enhance screening...

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