A Robust Framework of Chromosome Straightening with ViT-Patch GAN

• URL: http://arxiv.org/abs/2203.02901v2
• Date: Tue, 16 May 2023 07:12:13 GMT
• Title: A Robust Framework of Chromosome Straightening with ViT-Patch GAN
• Authors: Sifan Song, Jinfeng Wang, Fengrui Cheng, Qirui Cao, Yihan Zuo, Yongteng Lei, Ruomai Yang, Chunxiao Yang, Frans Coenen, Jia Meng, Kang Dang, Jionglong Su
• Abstract summary: Chromosome straightening is an important step for subsequent karyotype construction, pathological diagnosis and cytogenetic map development. 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 proposed method achieves better performance on Fr'eche't Inception Distance (FID), Learned Perceptual Image Patch Similarity (LPIPS) and downstream chromosome classification accuracy, and shows excellent generalization capability on a large dataset.
• Score: 5.657051014432275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Fr\'echet Inception Distance (FID), Learned Perceptual Image Patch Similarity (LPIPS) and downstream chromosome classification accuracy, and shows excellent generalization capability on a large dataset.

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