تقرير
A Robust Framework of Chromosome Straightening with ViT-Patch GAN
| العنوان: | A Robust Framework of Chromosome Straightening with ViT-Patch GAN |
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| المؤلفون: | Song, Sifan, Wang, Jinfeng, Cheng, Fengrui, Cao, Qirui, Zuo, Yihan, Lei, Yongteng, Yang, Ruomai, Yang, Chunxiao, Coenen, Frans, Meng, Jia, Dang, Kang, Su, Jionglong |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: Camera-ready version for IEEE ISBI2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2203.02901 |
| رقم الأكسشن: | edsarx.2203.02901 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |