染色体的分类是核型分析的重要任务之一。因其柔软易弯曲，且类间差异小、类内差异大等特点，其精准分类仍然是一个具有挑战性的难题。对此，提出一种基于网格重构学习(Grid reconstruction learning，GRiCoL)的染色体分类模型。该模型首先将染色体图像网格化，提取局部分类特征;然后通过重构网络对全局特征进行二次提取;最后完成分类。相比于现有几种先进方法，GRiCoL同时兼顾局部和全局特征提取更有效的分类特征，有效改善染色体弯曲导致的分类性能下降，参数规模合理。通过基于G带、荧光原位杂交(Fluorescence in situ hybridization，FISH)、Q带染色体公开数据集的实验表明:GRiCoL能够更好地弱化染色体弯曲带来的影响，在不同数据集上的分类准确度均优于现有分类方法。
A Grid Reconstruction Learning Model for Chromosome Classification
Chromosome classification is one of the key tasks of karyotype analysis.However,due to chromosomes are flexible hence exhibit less difference between different types while significant difference within same type,accur-ate classification of chromosome remains a challenging issue.In this paper,a chromosome classification model based on grid reconstruction learning(GRiCoL)is proposed.To weaken the impact of the bendy state,chromosome im-ages are first grid-enabled for feature extraction separately.Subsequentially,global features are extracted for the second time by reconstruction network,which is followed by classification.Compared with the state-of-the-art methods,the proposed GRiCoL can get more efficient discriminable features based on both local and global fea-tures,therefore can overcome the adverse effects of bandy form of chromosome with reasonable parameter scale.Ex-periments on public G band,fluorescence in situ hybridization(FISH)as well as Q band chromosome datasets show that GRiCoL can extract discriminative features that weaken the bending of chromosomes more efficiently,mean-while,higher performance was obtained as compared to current classification algorithms.
万方数据
维普
