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              基于網格重構學習的染色體分類模型

              張林 易先鵬 王廣杰 范心宇 劉輝 王雪松

              張林, 易先鵬, 王廣杰, 范心宇, 劉輝, 王雪松. 基于網格重構學習的染色體分類模型. 自動化學報, 2021, 48(x): 1001?1009 doi: 10.16383/j.aas.c210303
              引用本文: 張林, 易先鵬, 王廣杰, 范心宇, 劉輝, 王雪松. 基于網格重構學習的染色體分類模型. 自動化學報, 2021, 48(x): 1001?1009 doi: 10.16383/j.aas.c210303
              Zhang Lin, Yi Xian-Peng, Wang Guang-Jie, Fan Xin-Yu, Liu Hui, WANG Xue-Song. A grid reconstruction learning model for chromosome classification. Acta Automatica Sinica, 2021, 48(x): 1001?1009 doi: 10.16383/j.aas.c210303
              Citation: Zhang Lin, Yi Xian-Peng, Wang Guang-Jie, Fan Xin-Yu, Liu Hui, WANG Xue-Song. A grid reconstruction learning model for chromosome classification. Acta Automatica Sinica, 2021, 48(x): 1001?1009 doi: 10.16383/j.aas.c210303

              基于網格重構學習的染色體分類模型

              doi: 10.16383/j.aas.c210303
              基金項目: 國家自然科學基金(61971422, 31871337)資助
              詳細信息
                作者簡介:

                張林:中國礦業大學信息與控制工程學院教授. 主要研究方向為生物信息學、醫學圖像處理、機器學習. E-mail: lin.zhang@cumt.edu.cn

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

                王廣杰:中國礦業大學信息與控制工程學院碩士研究生. 主要研究方向醫學圖像處理. E-mail: guangjie.wang@cumt.edu.cn

                范心宇:中國礦業大學信息與控制工程學院博士研究生. 主要研究方向圖像處理. E-mail: xinyu.fan@cumt.edu.cn

                劉輝:中國礦業大學信息與控制工程學院副教授. 主要研究方向為生物信息學、醫學圖像處理、機器學習. E-mail: hui.liu@cumt.edu.cn

                王雪松:中國礦業大學信息與控制工程學院教授. 主要研究方向為人工智能、機器學習. 本文通訊作者. E-mail: wangxuesongcumt@163.com

              A Grid Reconstruction Learning Model for Chromosome Classification

              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: 染色體的分類識別是核型分析的重要任務之一. 因其柔軟易彎曲, 且類間差異小、類內差異大等特點, 其精準分類已成為挑戰性難題. 本文提出基于網格重構學習(GRid reConstruction learning, GRiCoL)的染色體分類模型. 該模型首先將染色體圖像網格化, 提取局部分類特征; 再通過重構網絡對全局特征進行二次提取, 最后完成分類. 相比于現有幾種方法, GRiCoL同時兼顧局部和全局特征提取更有效的分類特征, 有效改善染色體彎曲導致的分類性能下降, 參數規模合理. 通過基于G帶、熒光原位雜交、Q帶染色體公開數據集的實驗表明: GRiCoL能夠更好地弱化染色體彎曲帶來的影響, 在不同數據集上的分類準確度均優于現有分類方法.
              • 圖  1  基于網格重構學習的染色體分類模型

                Fig.  1  Chromosome classification model based on grid reconstruction learning

                圖  2  染色體圖像網格化效果((a) N = 2; (b) N = 3; (c) N = 4; (d) N = 3且重疊分割)

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

                圖  3  重構網絡模型

                Fig.  3  Reconstruction Learning Model

                圖  4  染色體圖像((a) G帶圖; (b) FISH圖; (c) Q帶圖)

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

                圖  5  骨干網絡第50層特征的導向反向傳播可視化 (a) G帶、(b) FISH、(c) Q帶 導向反向傳播圖

                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

                圖  6  特征的t-SNE降維表示

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

                表  1  交疊網格設計的分類性能對比

                Table  1  Classification performance comparison between grid with and without overlapping

                模型G帶FISHQ帶
                無交疊GRiCoL98.1%96.2%95.3%
                GRiCoL99.5%97.2%97.3%
                p2.66e-220.521.71e-8
                下載: 導出CSV

                表  2  不同N情況下分類性能的對比

                Table  2  Classification performance comparison between grids with different N

                NG帶(%)FISH(%)Q帶(%)Gflops參數量
                298.596.195. 811.522.1M
                399.597.297. 326.027.5M
                499.297.897. 646.335.0M
                下載: 導出CSV

                表  3  各模型分類性能對比

                Table  3  Classification performance comparison between different models

                模型G帶(%)FISH(%)Q帶(%)
                基線[34]93.492.087.8
                基線[35]95.393.491.7
                CIRNet95.9883.386.5
                ResNet5086.492.695.3
                [29]94.793.787.7
                [30]94.0
                GRiL98.395.895.9
                GRiCoL99.597.297.3
                下載: 導出CSV
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                        • 收稿日期:  2021-04-09
                        • 錄用日期:  2021-12-02
                        • 網絡出版日期:  2022-02-04

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