基于網(wǎng)格重構學(xué)習的染色體分類(lèi)模型

張林; 易先鵬; 王廣杰; 范心宇; 劉輝; 王雪松

doi:10.16383/j.aas.c210303

基于網(wǎng)格重構學(xué)習的染色體分類(lèi)模型

doi: 10.16383/j.aas.c210303

張林^{1, 2,},
易先鵬^2,,
王廣杰^2,,
范心宇^2,,
劉輝^{1, 2,},
王雪松^{1, 2,}

1.
中國礦業(yè)大學(xué)地下空間智能控制教育部工程研究中心徐州 221116
2.
中國礦業(yè)大學(xué)信息與控制工程學(xué)院徐州 221116

基金項目: 國家自然科學(xué)基金(61971422, 31871337)資助

詳細信息

作者簡(jiǎn)介:
張林：中國礦業(yè)大學(xué)信息與控制工程學(xué)院教授. 主要研究方向為生物信息學(xué), 醫學(xué)圖像處理, 機器學(xué)習. E-mail: lin.zhang@cumt.edu.cn

易先鵬：中國礦業(yè)大學(xué)信息與控制工程學(xué)院碩士研究生. 主要研究方向為醫學(xué)圖像處理. E-mail: xianpeng.yi@cumt.edu.cn

王廣杰：中國礦業(yè)大學(xué)信息與控制工程學(xué)院碩士研究生. 主要研究方向為醫學(xué)圖像處理. E-mail: guangjie.wang@cumt.edu.cn

范心宇：中國礦業(yè)大學(xué)信息與控制工程學(xué)院博士研究生. 主要研究方向為圖像處理. E-mail: xinyu.fan@cumt.edu.cn

劉輝：中國礦業(yè)大學(xué)信息與控制工程學(xué)院副教授. 主要研究方向為生物信息學(xué), 醫學(xué)圖像處理, 機器學(xué)習. E-mail: hui.liu@cumt.edu.cn

王雪松：中國礦業(yè)大學(xué)信息與控制工程學(xué)院教授. 主要研究方向為人工智能, 機器學(xué)習. 本文通信作者. E-mail: wangxuesongcumt@163.com

計量
- 文章訪(fǎng)問(wèn)數: 537
- HTML全文瀏覽量: 285
- PDF下載量: 56
- 被引次數: 0
出版歷程
- 收稿日期: 2021-04-09
- 錄用日期: 2021-12-02
- 網(wǎng)絡(luò )出版日期: 2022-02-04
- 刊出日期: 2024-10-21

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 221116
2.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116

Funds: Supported by National Natural Science Foundation of 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 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 is 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 is medical image processing

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

LIU Hui　Associate professor at the School of Information and Control Engineering, China University of Mining and Technology. His research interest covers bioinformatics, 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 research interest covers artificial intelligence and machine learning. Corresponding author of this paper

摘要

摘要: 染色體的分類(lèi)是核型分析的重要任務(wù)之一. 因其柔軟易彎曲, 且類(lèi)間差異小、類(lèi)內差異大等特點(diǎn), 其精準分類(lèi)仍然是一個(gè)具有挑戰性的難題. 對此, 提出一種基于網(wǎng)格重構學(xué)習(Grid reconstruction learning, GRiCoL)的染色體分類(lèi)模型. 該模型首先將染色體圖像網(wǎng)格化, 提取局部分類(lèi)特征; 然后通過(guò)重構網(wǎng)絡(luò )對全局特征進(jìn)行二次提取; 最后完成分類(lèi). 相比于現有幾種先進(jìn)方法, GRiCoL同時(shí)兼顧局部和全局特征提取更有效的分類(lèi)特征, 有效改善染色體彎曲導致的分類(lèi)性能下降, 參數規模合理. 通過(guò)基于G帶、熒光原位雜交 (Fluorescence in situ hybridization, FISH)、Q帶染色體公開(kāi)數據集的實(shí)驗表明: GRiCoL能夠更好地弱化染色體彎曲帶來(lái)的影響, 在不同數據集上的分類(lèi)準確度均優(yōu)于現有分類(lèi)方法.
- 核型分析 /
- 染色體分類(lèi) /
- 特征重構 /
- 網(wǎng)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 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.
- Karyotype analysis /
- chromosome classification /
- feature reconstruction /
- gridding

HTML全文

圖 1 基于網(wǎng)格重構學(xué)習的染色體分類(lèi)模型

Fig. 1 Chromosome classification model based on grid reconstruction learning

下載: 全尺寸圖片幻燈片

圖 2 染色體圖像網(wǎng)格化效果

Fig. 2 Gridding effect of chromosome image

下載: 全尺寸圖片幻燈片

圖 3 重構網(wǎng)絡(luò )模型

Fig. 3 Reconstruction network model

下載: 全尺寸圖片幻燈片

圖 4 染色體圖像

Fig. 4 Chromosome images

下載: 全尺寸圖片幻燈片

圖 5 骨干網(wǎng)絡(luò )第50層特征的導向反向傳播可視化

Fig. 5 Visualization of guided back propagation of the 50th layer features of the backbone network

下載: 全尺寸圖片幻燈片

圖 6 特征的t-SNE降維表示

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

下載: 全尺寸圖片幻燈片

表 1 交疊網(wǎng)格設計的分類(lèi)性能對比

Table 1 Classification performance comparison between grid with and without overlapping

模型 G帶 FISH Q帶

無(wú)交疊 GRiCoL 98.1% 96.2% 95.3%
GRiCoL 99.5% 97.2% 97.3%
p值 2.66e?22 0.52 1.71e?8

下載: 導出CSV

表 2 不同N數量下分類(lèi)性能的對比

Table 2 Classification performance comparison between grids with different N

N G帶(%) FISH (%) Q帶(%) Gflops 參數量(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

下載: 導出CSV

表 3 不同模型分類(lèi)性能對比(%)

Table 3 Classification performance comparison between different models (%)

模型 G帶 FISH Q帶

基線(xiàn)^[34] 93.4 92.0 87.8
基線(xiàn)^[35] 95.3 93.4 91.7
CIRNet 96.0 83.3 86.5
ResNet50 86.4 92.6 95.3
文獻[29] 94.7 93.7 87.7
文獻[30] — — 94.0
GRiL 98.3 95.8 95.9
GRiCoL 99.5 97.2 97.3

下載: 導出CSV

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基于網(wǎng)格重構學(xué)習的染色體分類(lèi)模型

doi: 10.16383/j.aas.c210303

計量

A Grid Reconstruction Learning Model for Chromosome Classification

計量

目錄

模型	G帶	FISH	Q帶
無(wú)交疊 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帶(%)	FISH (%)	Q帶(%)	Gflops	參數量(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

模型	G帶	FISH	Q帶
基線(xiàn)^[34]	93.4	92.0	87.8
基線(xiàn)^[35]	95.3	93.4	91.7
CIRNet	96.0	83.3	86.5
ResNet50	86.4	92.6	95.3
文獻[29]	94.7	93.7	87.7
文獻[30]	—	—	94.0
GRiL	98.3	95.8	95.9
GRiCoL	99.5	97.2	97.3

留言板

基于網(wǎng)格重構學(xué)習的染色體分類(lèi)模型

doi: 10.16383/j.aas.c210303

計量

出版歷程

A Grid Reconstruction Learning Model for Chromosome Classification

計量

出版歷程

目錄