A Robust Framework of Chromosome Straightening with ViT-Patch GAN
This addresses a domain-specific challenge in medical imaging for cytogenetics, with incremental improvements in detail retention and generalization.
The paper tackles the problem of robust chromosome straightening for karyotype construction and diagnosis by proposing ViT-Patch GAN, which achieves better performance on metrics like FID, LPIPS, and classification accuracy while retaining shape and banding details.
Chromosomes carry the genetic information of humans. They exhibit non-rigid and non-articulated nature with varying degrees of curvature. Chromosome straightening is an important step for subsequent karyotype construction, pathological diagnosis and cytogenetic map development. However, robust chromosome straightening remains challenging, due to the unavailability of training images, distorted chromosome details and shapes after straightening, as well as poor generalization capability. In this paper, we propose a novel architecture, ViT-Patch GAN, consisting of a self-learned motion transformation generator and a Vision Transformer-based patch (ViT-Patch) discriminator. The generator learns the motion representation of chromosomes for straightening. With the help of the ViT-Patch discriminator, the straightened chromosomes retain more shape and banding pattern details. The experimental results show that the proposed method achieves better performance on Fréchet Inception Distance (FID), Learned Perceptual Image Patch Similarity (LPIPS) and downstream chromosome classification accuracy, and shows excellent generalization capability on a large dataset.