基于网格重构学习的染色体分类模型

张林; 易先鹏; 王广杰; 范心宇; 刘辉; 王雪松

doi:10.16383/j.aas.c210303

基于网格重构学习的染色体分类模型

doi: 10.16383/j.aas.c210303

张林^{1, 2,},
易先鹏^2,,
王广杰^2,,
范心宇^2,,
刘辉^{1, 2,},
王雪松^{1, 2,}

1.
中国矿业大学地下空间智能控制教育部工程研究中心江苏徐州 221116
2.
中国矿业大学信息与控制工程学院江苏徐州 221116

基金项目: 国家自然科学基金(61971422, 31871337)资助

详细信息

作者简介:
张林：中国矿业大学信息与控制工程学院教授. 主要研究方向为生物信息学、医学图像处理、机器学习. E-mail: lin.zhang@cumt.edu.cn

易先鹏：中国矿业大学信息与控制工程学院硕士研究生. 主要研究方向为医学图像处理. E-mail: xianpeng.yi@cumt.edu.cn

王广杰：中国矿业大学信息与控制工程学院硕士研究生. 主要研究方向医学图像处理. E-mail: guangjie.wang@cumt.edu.cn

范心宇：中国矿业大学信息与控制工程学院博士研究生. 主要研究方向图像处理. E-mail: xinyu.fan@cumt.edu.cn

刘辉：中国矿业大学信息与控制工程学院副教授. 主要研究方向为生物信息学、医学图像处理、机器学习. E-mail: hui.liu@cumt.edu.cn

王雪松：中国矿业大学信息与控制工程学院教授. 主要研究方向为人工智能、机器学习. 本文通讯作者. E-mail: wangxuesongcumt@163.com

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出版历程
- 收稿日期: 2021-04-09
- 录用日期: 2021-12-02
- 网络出版日期: 2022-02-04

A Grid Reconstruction Learning Model for Chromosome Classification

ZHANG Lin^{1, 2
,},
YI Xian-Peng^2
,,
WANG Guang-Jie^2
,,
FAN Xin-Yu^2
,,
LIU Hui^{1, 2
,},
WANG Xue-Song^{1, 2
,}

1.
Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou, Jiangsu, 221116
2.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, 221116

Funds: Supported by National Natural Science Foundation of P. R. China (61971422, 31871337)

More Information

Author Bio:
ZHANG Lin　Professor at the School of Information and Control Engineering, China University of Mining and Technology. Her main research interest covers Bioinformatics, medical image processing and machine learning

YI Xian-Peng　Master student at the School of Information and Control Engineering, China University of Mining and Technology. His main research interest covers medical image processing

WANG Guang-Jie　Master student at the School of Information and Control Engineering, China University of Mining and Technology. His main research interest covers medical image processing

FAN Xin-Yu　Ph.D student at the School of Information and Control Engineering, China University of Mining and Technology. Her main research interest covers image processing

LIU Hui　Associate professor at the School of Information and Control Engineering, China University of Mining and Technology. His main research interest covers Bioinformatics and medical image processing and machine learning

WANG Xue-Song　Professor at the School of Information and Control Engineering, China University of Mining and Technology. Her main research interest covers artificial intelligence and machine learning. Corresponding author of this paper

摘要

摘要: 染色体的分类识别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, 其精准分类已成为挑战性难题. 本文提出基于网格重构学习(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; 再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、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.
- 核型分析 /
- 染色体分类 /
- 特征重构 /
- 网格化
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 GRid reConstruction Learning algorithm named 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 as well as Q band 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.
- 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

下载: 全尺寸图片幻灯片

图 2 染色体图像网格化效果((a) N = 2; (b) N = 3; (c) N = 4; (d) N = 3且重叠分割)

Fig. 2 Gridding effect of chromosome image((a) N = 2; (b) N = 3; (c) N = 4; (d) N = 3 and overlapping grid)

下载: 全尺寸图片幻灯片

图 3 重构网络模型

Fig. 3 Reconstruction Learning Model

下载: 全尺寸图片幻灯片

图 4 染色体图像((a) G带图; (b) FISH图; (c) Q带图)

Fig. 4 Chromosome image ((a) G band image; (b)FISH image; (c) Q band image)

下载: 全尺寸图片幻灯片

图 5 骨干网络第50层特征的导向反向传播可视化 (a) G带、(b) FISH、(c) Q带导向反向传播图

Fig. 5 Visualization of Guided Back Propagation of the 50th Layer Features of the Backbone Network (a) G band、(b) FISH、(c) Q band images of Guided Back Propagation

下载: 全尺寸图片幻灯片

图 6 特征的t-SNE降维表示

Fig. 6 Representation of features dimensionality reduced by t-SNE

下载: 全尺寸图片幻灯片

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

下载: 导出CSV

表 2 不同N情况下分类性能的对比

Table 2 Classification performance comparison between grids with different N

N G带(%) FISH(%) Q带(%) Gflops 参数量

2 98.5 96.1 95. 8 11.5 22.1M
3 99.5 97.2 97. 3 26.0 27.5M
4 99.2 97.8 97. 6 46.3 35.0M

下载: 导出CSV

表 3 各模型分类性能对比

Table 3 Classification performance comparison between different models

模型 G带(%) FISH(%) Q带(%)

基线[34] 93.4 92.0 87.8
基线[35] 95.3 93.4 91.7
CIRNet 95.98 83.3 86.5
ResNet50 86.4 92.6 95.3
[29] 94.7 93.7 87.7
[30] — — 94.0
GRiL 98.3 95.8 95.9
GRiCoL 99.5 97.2 97.3

下载: 导出CSV

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基于网格重构学习的染色体分类模型

doi: 10.16383/j.aas.c210303

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A Grid Reconstruction Learning Model for Chromosome Classification

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目录

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

N	G带(%)	FISH(%)	Q带(%)	Gflops	参数量
2	98.5	96.1	95. 8	11.5	22.1M
3	99.5	97.2	97. 3	26.0	27.5M
4	99.2	97.8	97. 6	46.3	35.0M

模型	G带(%)	FISH(%)	Q带(%)
基线[34]	93.4	92.0	87.8
基线[35]	95.3	93.4	91.7
CIRNet	95.98	83.3	86.5
ResNet50	86.4	92.6	95.3
[29]	94.7	93.7	87.7
[30]	—	—	94.0
GRiL	98.3	95.8	95.9
GRiCoL	99.5	97.2	97.3

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基于网格重构学习的染色体分类模型

doi: 10.16383/j.aas.c210303

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A Grid Reconstruction Learning Model for Chromosome Classification

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