Ongoing Projects

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Bone2Gene

Bone2Gene AI aims to identify the unique imaging patterns linked to various bone disorders and support clinicians in the... Read more

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Photorealistic Synthetic Portraits

Here we show photorealistic synthetic portraits of certain rare diseases based on the cohort ... Read more

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Alagille Syndrome Project

Alagille syndrome is a rare genetic disorder that can affect multiple organs, including the liver, heart, and... Read more

Meta-learning for rare disorder diagnosis

Mar. 13th 2023

Principal Investigator: Dr. admin user

Institution: uni bonn

We are particularly enthusiastic about exploring the potential of applying meta-learning to solve the following two problems: 1) semi-supervised meta-learning with unlabeled data, and 2) meta-learning with side information, where the side information could be a collection of meta-data. In light of your work on rare disease detection, I believe that this scenario presents an excellent...

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

Feb. 17th 2023

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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Deep Facial Phenotyping with Mixup Augmentation

Aug. 2nd 2022

Principal Investigator: Mr. Jonathan Campbell

Institution: University of Oxford

The classification of genetic disorders from face images has the potential to assist with early diagnosis and effective treatment. A key objective in this field is to enhance the accuracy and robustness of deep learning models in classifying genetic disorders from face images for disorders that are not represented in the training data. In this paper, we propose the use of input mixup...

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