Synthetic Image Oversampling for Rare-Class Representation Learning in Deep Facial Phenotyping
Recent deep facial phenotyping methods can support the recognition of genetic syndromes, but patient images are often scarce and unevenly distributed across disorders.
ProofPaper ↗
Key points
- Synthetic facial images offer a way to supplement the training data without collecting additional patient photographs.
- Their usefulness, however, depends on whether they improve recognition of real patients: generated images may introduce artifacts or repeat features already represented in the training set.
- We investigate disease-specific synthetic oversampling using images from the Phenotypic Disease Image Database to augment a subset of the GestaltMatcher Database.
- We compare increasing augmentation levels in an image-based model, GestaltMatcher-Arc, and a multimodal model, GestaltMML, while keeping the real training and patient-disjoint evaluation partitions fixed.
Sources (1)
- [1]Synthetic Image Oversampling for Rare-Class Representation Learning in Deep Facial PhenotypingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 04:43 PM
Recent deep facial phenotyping methods can support the recognition of genetic syndromes, but patient images are often scarce and unevenly distributed across disorders.
Synthetic facial images offer a way to supplement the training data without collecting additional patient photographs.
Extractive summary: sentences quoted from the sources.