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.

提出了一种针对可靠染色体分类训练的监督对比学习策略，通过在潜在空间中提取细粒度的染色体嵌入，扩大了类间边界和减少了类内差异，提高了预测染色体类型的准确性，可以显著提升深度网络的泛化性能。

细粒度染色体识别的监督对比学习

Supervised Contrastive Learning for Fine-grained Chromosome Recognition

Karyotyping is of importance for detecting chromosomal aberrations in human
disease. However, chromosomes easily appear curved in microscopic images, which
prevents cytogeneticists from analyzing chromosome types. To address this
issue, we propose a framework for chromosome straightening, which comprises a
preliminary processing algorithm and a generative model called masked
conditional variational autoencoders (MC-VAE). The processing method utilizes
patch rearrangement to address the difficulty in erasing low degrees of
curvature, providing reasonable preliminary results for the MC-VAE. The MC-VAE
further straightens the results by leveraging chromosome patches conditioned on
their curvatures to learn the mapping between banding patterns and conditions.
During model training, we apply a masking strategy with a high masking ratio to
train the MC-VAE with eliminated redundancy. This yields a non-trivial
reconstruction task, allowing the model to effectively preserve chromosome
banding patterns and structure details in the reconstructed results. Extensive
experiments on three public datasets with two stain styles show that our
framework surpasses the performance of state-of-the-art methods in retaining
banding patterns and structure details. Compared to using real-world bent
chromosomes, the use of high-quality straightened chromosomes generated by our
proposed method can improve the performance of various deep learning models for
chromosome classification by a large margin. Such a straightening approach has
the potential to be combined with other karyotyping systems to assist
cytogeneticists in chromosome analysis.

本文提出了一种染色体直线化的框架，使用自编码器对染色体进行重建，使其保留带状图案和结构细节，用于染色体分类和分析。