Visual Encoders for Generalized Chromosome Recognition

Chromosome recognition is a vital task in karyotyping, crucial for birth defect diagnosis and advancing biomedical research. However, developing generalizable classification models faces significant challenges due to the inter-class similarities, intra-class variations, and stark distribution discrepancies across multi-center data. To bridge this gap, we propose a supervised contrastive learning strategy aimed at training robust domain-generalized encoders for accurate chromosome classification. Our model trained using over 3,700,000 chromosome images from multiple centers, excels at extracting fine-grained chromosomal embeddings. These embeddings effectively widen inter-class margins and minimize intra-class variations, thereby enhancing the distinctiveness crucial for precise chromosome type recognition. We comprehensively validate our domain-generalized encoders on two additional large-scale datasets, demonstrating their substantial ability to improve generalization performance. We release our code and pre-trained model weights at https://github.com/RuijiaChang/Chromosome-SCL-Encoder.