Supervised Contrastive Learning for Fine-grained Chromosome Recognition

• URL: http://arxiv.org/abs/2312.07623v1
• Date: Tue, 12 Dec 2023 06:12:21 GMT
• Title: Supervised Contrastive Learning for Fine-grained Chromosome Recognition
• Authors: Ruijia Chang, Suncheng Xiang, Chengyu Zhou, Kui Su, Dahong Qian, Jun Wang
• Abstract summary: Chromosome recognition is an essential task in karyotyping, which plays a vital role in birth defect diagnosis and biomedical research. Existing classification methods face significant challenges due to the inter-class similarity and intra-class variation of chromosomes. We propose a supervised contrastive learning strategy that is tailored to train model-agnostic deep networks for reliable chromosome classification.
• Score: 7.427070103487921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chromosome recognition is an essential task in karyotyping, which plays a vital role in birth defect diagnosis and biomedical research. However, existing classification methods face significant challenges due to the inter-class similarity and intra-class variation of chromosomes. To address this issue, we propose a supervised contrastive learning strategy that is tailored to train model-agnostic deep networks for reliable chromosome classification. This method enables extracting fine-grained chromosomal embeddings in latent space. These embeddings effectively expand inter-class boundaries and reduce intra-class variations, enhancing their distinctiveness in predicting chromosome types. On top of two large-scale chromosome datasets, we comprehensively validate the power of our contrastive learning strategy in boosting cutting-edge deep networks such as Transformers and ResNets. Extensive results demonstrate that it can significantly improve models' generalization performance, with an accuracy improvement up to +4.5%. Codes and pretrained models will be released upon acceptance of this work.

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