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摘要: 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.Abstract: 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, accurate 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 images 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 features, therefore can overcome the adverse effects of bandy form of chromosome with reasonable parameter scale. Experiments 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, meanwhile, higher performance was obtained as compared to current classification algorithms.
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Key words:
- Karyotype analysis /
- chromosome classification /
- feature reconstruction /
- gridding
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图 1 基于网格重构学习的染色体分类模型
Fig. 1 Chromosome classification model based on grid reconstruction learning
图 5 骨干网络第50层特征的导向反向传播可视化
Fig. 5 Visualization of guided back propagation of the 50th layer features of the backbone network
表 1 交叠网格设计的分类性能对比
Table 1 Classification performance comparison between grid with and without overlapping
模型 G带 FISH Q带 无交叠 GRiCoL 98.1% 96.2% 95.3% GRiCoL 99.5% 97.2% 97.3% p值 2.66e−22 0.52 1.71e−8 
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表 2 不同N数量下分类性能的对比
Table 2 Classification performance comparison between grids with different N
N G带(%) FISH (%) Q带(%) Gflops 参数量(M) 2 98.5 96.1 95.8 11.5 22.1 3 99.5 97.2 97.3 26.0 27.5 4 99.2 97.8 97.6 46.3 35.0
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