English
往期目录
新闻公告
More
化学进展 2022, Vol. 34 Issue (12): 2561-2572 DOI: 10.7536/PC220512   后一篇

• 综述 •

可解释深度学习在光谱和医学影像分析中的应用

刘煦阳1, 段潮舒1, 蔡文生1,2, 邵学广1,2,*()   

  1. 1 南开大学化学学院分析科学研究中心,天津市生物传感与分子识别重点实验室,药物化学生物学国家重点实验室 天津 300071
    2 物质绿色创造与制造海河实验室 天津 300192
  • 收稿日期:2022-05-09 修回日期:2022-07-07 出版日期:2022-12-24 发布日期:2022-09-19
  • 通讯作者: 邵学广
  • 作者简介:

    邵学广 南开大学博士生导师,主要从事化学计量学、复杂体系近红外光谱分析、分子模拟的方法和应用研究,建立了化学因子分析、优化算法、小波分析、免疫算法、温控近红外光谱、重要性采样和自由能计算等方法。近年来,开展了近红外水光谱探针、高光谱成像以及基于人工智能的医学影像分析和分子模拟等方面的研究。

  • 基金资助:
    国家自然科学基金(22174075); 天津市自然科学基金(20JCYBJC01480); 物质绿色创造与制造海河实验室资助项目(ZYTS202105)
Richhtml ( 53 ) 下载PDF ( 460 ) 引用
导出

Explainable Deep Learning in Spectral and Medical Image Analysis

Xuyang Liu1, Chaoshu Duan1, Wensheng Cai1,2, Xueguang Shao1,2()   

  1. 1 Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University,Tianjin 300071, China
    2 Haihe Laboratory of Sustainable Chemical Transformations,Tianjin 300192, China
  • Received:2022-05-09 Revised:2022-07-07 Online:2022-12-24 Published:2022-09-19
  • Contact: Xueguang Shao
  • Supported by:
    National Natural Science Foundation of China(22174075); Natural Science Foundation of Tianjin, China(20JCYBJC01480); Haihe Laboratory of Sustainable Chemical Transformations(ZYTS202105)

深度学习是一种基于神经网络的建模方法,通过不同功能感知层的构建获得优化模型,提取大量数据的内在规律,实现端到端的建模。数据规模的增长和计算能力的提高促进了深度学习在光谱及医学影像分析中的应用,但深度学习模型可解释性的不足是阻碍其应用的关键因素。为克服深度学习可解释性的不足,研究者提出并发展了可解释性方法。根据解释原理的不同,可解释性方法划分为可视化方法、模型蒸馏及可解释模型,其中可视化方法及模型蒸馏属于外部解释算法,在不改变模型结构的前提下解释模型,而可解释模型旨在使模型结构可解释。本文从算法角度介绍了深度学习及三类可解释性方法的原理,综述了近三年深度学习及可解释性方法在光谱及医学影像分析中的应用。多数研究聚焦于可解释性方法的建立,通过外部算法揭示模型的预测机制并解释模型,但构建可解释模型方面的研究相对较少。此外,采用大量标记数据训练模型是目前的主流研究方式,但给数据的采集带来了巨大的负担。基于小规模数据的训练策略、增强模型可解释性的方法及可解释模型的构建仍是未来的发展趋势。

关键词： 深度学习, 可解释性方法, 神经网络, 医学影像分析, 光谱分析

Deep learning is a modeling method based on neural network, which is constructed of multiple different functional perception layers and optimized by learning the inherent regularity of a large amount of data to achieve end-to-end modeling. The growth of data and the improvement of computing power promoted the applications of deep learning in spectral and medical image analysis. The lack of interpretability of the constructed models, however, constitutes an obstacle to their further development and applications. To overcome this obstacle of deep learning, various interpretability methods are proposed. According to different principles of explanation, interpretability methods are divided into three categories: visualization methods, model distillation, and interpretable models. Visualization methods and model distillation belong to external algorithms, which interpret a model without changing its structure, while interpretable models aim to make the model structure interpretable. In this review, the principles of deep learning and three interpretability methods are introduced from the perspective of algorithms. Moreover, the applications of the interpretability methods in spectral and medical image analysis in the past three years are summarized. In most studies, external algorithms were developed to make the models explainable, and these methods were found to be able to provide reasonable explanation for the abilities of the deep learning models. However, few studies attempt to construct interpretable algorithms within networks. Furthermore, most studies try to train the model through collecting large amounts of labeled data, which leads to huge costs in both labor and expenses. Therefore, training strategies with small data sets, approaches to enhance the interpretability of models, and the construction of interpretable deep learning architectures are still required in future work.

Contents

1 Introduction

2 Principle and algorithm

3 Interpretability method

3.1 Visualization method

3.2 Model distillation

3.3 Interpretable model

4 Spectral analysis

5 Medical image analysis

5.1 Segmentation

5.2 Disease diagnosis

6 Summary and outlook

Key words: deep learning, interpretability method, neural network, medical image analysis, spectral analysis
()
图1 CNN结构及计算过程
Fig. 1 Structure and calculation process of CNN
图2 RNN结构及计算过程
Fig. 2 Structure and calculation process of RNN
图3 grad-CAM算法的计算流程示例
Fig. 3 Example of calculation process of grad-CAM algorithm
图4 积分梯度算法的计算流程示例
Fig. 4 Example of calculation process of integrated gradient algorithm
图5 遮挡灵敏性算法计算得到的特征重要性分布
Fig. 5 Visualization of feature importance distribution by occlusion sensitivity algorithm
图6 LIME计算流程示例
Fig. 6 Example of calculation process of LIME algorithm
图7 SENNs的模型解释机制
Fig. 7 Model interpretation mechanism of SENNs
图8 结合PCA的神经网络分类策略[49]
Fig. 8 Neural network classification strategy combined with PCA[49]. Reprinted with permission from [49]. Copyright 2022 American Chemical Society
图9 CycleGAN对近红外IIb区荧光成像的预测结果[55]
Fig. 9 NIR-IIb image predicted by CycleGAN[55]
[1]
Miotto R, Wang F, Wang S, Jiang X Q, Dudley J T. Brief. Bioinform., 2018, 19(6): 1236.
[2]
Senior A W, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C L, Žídek A, Nelson A W R, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones D T, Silver D, Kavukcuoglu K, Hassabis D. Nature, 2020, 577(7792): 706.
[3]
Jing R Y, Li Y Z, Xue L, Liu F J, Li M L, Luo J S. J. Chem. Inf. Model., 2020, 60(8): 3755.
[4]
Yang X, Wang Y F, Byrne R, Schneider G, Yang S Y. Chem. Rev., 2019, 119(18): 10520.

doi: 10.1021/acs.chemrev.8b00728     pmid: 31294972
[5]
Zhang X L, Lin T, Xu J F, Luo X, Ying Y B. Anal. Chimica Acta, 2019, 1058: 48.
[6]
Li W Z, Miao W, Cui J X, Fang C, Su S T, Li H Z, Hu L H, Lu Y H, Chen G H. J. Chem. Inf. Model., 2019, 59(5): 1849.
[7]
Li W Z, Wang D H, Yang Z R, Zhang H J, Hu L H, Chen G H. J. Chem. Inf. Model., 2021, acs.jcim.1c01305.
[8]
Yang J, Xu J F, Zhang X L, Wu C Y, Lin T, Ying Y B. Anal. Chimica Acta, 2019, 1081: 6.
[9]
Shao X G, Leung A K M, Chau F T. Acc. Chem. Res., 2003, 36(4): 276.
[10]
Cai W S, Li Y K, Shao X G. Chemom. Intell. Lab. Syst., 2008, 90(2): 188.
[11]
Zhang J, Cui X Y, Cai W S, Shao X G. Sci. China Chem., 2019, 62(2): 271.

doi: 10.1007/s11426-018-9368-9    
[12]
Kermany D S, Goldbaum M, Cai W J, Valentim C C S, Liang H Y, Baxter S L, McKeown A, Yang G, Wu X K, Yan F B, Dong J, Prasadha M K, Pei J, Ting M Y L, Zhu J, Li C, Hewett S, Dong J, Ziyar I, Shi A, Zhang R Z, Zheng L H, Hou R, Shi W, Fu X, Duan Y O, Huu V A N, Wen C, Zhang E D, Zhang C L, Li O L, Wang X B, Singer M A, Sun X D, Xu J, Tafreshi A, Lewis M A, Xia H M, Zhang K. Cell, 2018, 172(5): 1122.

doi: S0092-8674(18)30154-5     pmid: 29474911
[13]
Wang P, Xiao X, Glissen Brown J R, Berzin T M, Tu M T, Xiong F, Hu X, Liu P X, Song Y, Zhang D, Yang X, Li L P, He J, Yi X, Liu J J, Liu X G. Nat. Biomed. Eng., 2018, 2(10): 741.

doi: 10.1038/s41551-018-0301-3     pmid: 31015647
[14]
Shi Z, Miao C C, Schoepf U J, Savage R H, Dargis D M, Pan C W, Chai X, Li X L, Xia S, Zhang X, Gu Y, Zhang Y G, Hu B, Xu W D, Zhou C S, Luo S, Wang H, Mao L, Liang K M, Wen L L, Zhou L J, Yu Y Z, Lu G M, Zhang L J. Nat. Commun., 2020, 11: 6090.
[15]
He H, Yan S, Lyu D Y, Xu M X, Ye R Q, Zheng P, Lu X Y, Wang L, Ren B. Anal. Chem., 2021, 93(8): 3653.
[16]
Shen D G, Wu G R, Suk H I. Annu. Rev. Biomed. Eng., 2017, 19: 221.
[17]
McCulloch W S, Pitts W. Bull. Math. Biophys., 1943, 5(4): 115.
[18]
Rosenblatt F. Psychol. Rev., 1958, 65(6): 386.
[19]
Rumelhart D E, Hinton G E, Williams R J. California Univ San Diego La Jolla Inst for Cognitive Science, 1985, 1.
[20]
Long J, Shelhamer E, Darrell T. 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA. IEEE, 2015, 3431.
[21]
Ronneberger O, Fischer P, Brox T. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, 234.
[22]
Hochreiter S, Schmidhuber J. Neural Comput., 1997, 9(8): 1735.

doi: 10.1162/neco.1997.9.8.1735     pmid: 9377276
[23]
Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. arXiv preprint arXiv:1406.1078, 2014.
[24]
Bahdanau D, Cho K, Bengio Y. arXiv preprint arXiv:1409.0473, 2014.
[25]
Luong M-T, Pham H, Manning C D. arXiv preprint arXiv:1508.04025, 2015.
[26]
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł, Polosukhin I. Advances in neural information processing systems, 2017.
[27]
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Advances in neural information processing systems, 2014.
[28]
Isola P, Zhu J Y, Zhou T H, Efros A A. 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA. IEEE, 2017, 5967.
[29]
Zhu J Y, Park T, Isola P, Efros A A. 2017 IEEE International Conference on Computer Vision. Venice, Italy. IEEE, 2017, 2242.
[30]
Zhou B L, Khosla A, Lapedriza A, Oliva A, Torralba A. 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA. IEEE,2016, 2921.
[31]
Selvaraju R R, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017, 618.
[32]
Sundararajan M, Taly A, Yan Q Q. International Conference on Machine Learning, 2017, 3319.
[33]
Zeiler M D, Fergus R. European Conference on Computer Vision, 2014, 818.
[34]
Fong R C, Vedaldi A. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2017, 3429.
[35]
Zintgraf L M, Cohen T S, Adel T, Welling M. arXiv preprint arXiv:1702.04595, 2017.
[36]
Ribeiro M T, Singh S, Guestrin C.Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco California USA. New York, NY, USA: ACM, 2016, 1135.
[37]
Lundberg S M, Lee S-I.Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017, 4768.
[38]
Frosst N, Hinton G. arXiv preprint arXiv:1711.09784, 2017.
[39]
Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y. International Conference on Machine Learning, 2015, 2048.
[40]
Zellers R, Bisk Y, Farhadi A, Choi Y. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA. IEEE, 2019, 6713.
[41]
Alvarez-Melis D, Jaakkola T S. arXiv preprint arXiv:1806.07538, 2018.
[42]
Bian X H, Li S J, Shao X G, Liu P. Chemom. Intell. Lab. Syst., 2016, 158: 174.
[43]
Zhang M, Cai W S, Shao X G. Anal., 2011, 136(20): 4217.
[44]
Ju L, Lyu A H, Hao H X, Shen W, Cui H. Anal. Chem., 2019, 91(15): 9343.
[45]
Ni C, Wang D Y, Tao Y. Spectrochim. Acta, Part A, 2019, 209: 32.
[46]
Zhang C, Wu W Y, Zhou L, Cheng H, Ye X Q, He Y. Food Chem., 2020, 319: 126536.
[47]
Xiong Y R, Yang W Y, Liao H Y, Gong Z L, Xu Z Z, Du Y P, Li W. Chemom. Intell. Lab. Syst., 2022, 223: 104532.
[48]
Huang G Z, Yuan L M, Shi W, Chen X, Chen X J. Food Chem., 2022, 372: 131219.
[49]
Shin H, Oh S, Hong S, Kang M, Kang D, Ji Y G, Choi B H, Kang K W, Jeong H, Park Y, Hong S, Kim H K, Choi Y. ACS Nano, 2020, 14(5): 5435.
[50]
Shu C, Yan H S, Zheng W, Lin K, James A, Selvarajan S, Lim C M, Huang Z W. Anal. Chem., 2021, 93(31): 10898.
[51]
Huang J L, Wen J X, Zhou M J, Ni S, Le W, Chen G, Wei L, Zeng Y, Qi D J, Pan M, Xu J N, Wu Y, Li Z Y, Feng Y L, Zhao Z Q, He Z B, Li B, Zhao S N, Zhang B H, Xue P L, He S S, Fang K, Zhao Y Y, Du K. Anal. Chem., 2021, 93(26): 9174.
[52]
Yu S X, Li X, Lu W L, Li H F, Fu Y V, Liu F H. Anal. Chem., 2021, 93(32): 11089.
[53]
Zhou L, Zhang C, Taha M F, Wei X H, He Y, Qiu Z J, Liu Y F. Front. Plant Sci., 2020, 11: 575810.
[54]
Yang S, Li C X, Mei Y, Liu W, Liu R, Chen W L, Han D H, Xu K X. Front. Nutr., 2021, 8: 680627.
[55]
Ma Z R, Wang F F, Wang W Z, Zhong Y T, Dai H J. Deep learning for in vivo near-infrared imaging. Proc. Natl. Acad. Sci. U. S. A., 2021, 118(1): e2021446118.
[56]
Guo S X, Mayerhöfer T, Pahlow S, Hübner U, Popp J, Bocklitz T. Anal., 2020, 145(15): 5213.
[57]
Badrinarayanan V, Handa A, Cipolla R. arXiv preprint arXiv:1505.07293, 2015.
[58]
Fu J, Liu J, Tian H J, Li Y, Bao Y J, Fang Z W, Lu H Q. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA. IEEE, 2019, 3141.
[59]
Bouteldja N, Klinkhammer B M, Bülow R D, Droste P, Otten S W, von Stillfried S F, Moellmann J, Sheehan S M, Korstanje R, Menzel S, Bankhead P, Mietsch M, Drummer C, Lehrke M, Kramann R, Floege J, Boor P, Merhof D. J. Am. Soc. Nephrol., 2021, 32(1): 52.

doi: 10.1681/ASN.2020050597     pmid: 33154175
[60]
Wang X D, Chen Y, Gao Y S, Zhang H Q, Guan Z H, Dong Z, Zheng Y X, Jiang J R, Yang H Q, Wang L M, Huang X M, Ai L R, Yu W L, Li H W, Dong C S, Zhou Z, Liu X Y, Yu G Z. Nat. Commun., 2021, 12: 1637.
[61]
Zhang K, Liu X H, Shen J, Li Z H, Sang Y, Wu X W, Zha Y F, Liang W H, Wang C D, Wang K, Ye L S, Gao M, Zhou Z G, Li L, Wang J, Yang Z H, Cai H M, Xu J, Yang L, Cai W J, Xu W Q, Wu S X, Zhang W, Jiang S P, Zheng L H, Zhang X, Wang L, Lu L, Li J M, Yin H P, Wang W, Li O L, Zhang C, Liang L, Wu T, Deng R Y, Wei K, Zhou Y, Chen T, Lau J Y N, Fok M, He J X, Lin T X, Li W M, Wang G Y. Cell, 2020, 181(6): 1423.

doi: S0092-8674(20)30551-1     pmid: 32416069
[62]
Wang G Y, Liu X H, Shen J, Wang C D, Li Z H, Ye L S, Wu X W, Chen T, Wang K, Zhang X, Zhou Z G, Yang J, Sang Y, Deng R Y, Liang W H, Yu T, Gao M, Wang J, Yang Z H, Cai H M, Lu G M, Zhang L Y, Yang L, Xu W Q, Wang W, Olvera A, Ziyar I, Zhang C, Li O L, Liao W H, Liu J, Chen W, Chen W, Shi J C, Zheng L H, Zhang L J, Yan Z H, Zou X G, Lin G P, Cao G Q, Lau L L, Mo L, Liang Y, Roberts M, Sala E, Schönlieb C B, Fok M, Lau J Y N, Xu T, He J X, Zhang K, Li W M, Lin T X. Nat. Biomed. Eng., 2021, 5(8): 509.
[63]
Lee H, Yune S, Mansouri M, Kim M, Tajmir S H, Guerrier C E, Ebert S A, Pomerantz S R, Romero J M, Kamalian S, Gonzalez R G, Lev M H, Do S. Nat. Biomed. Eng., 2019, 3(3): 173.
[64]
Zhou D J, Tian F, Tian X D, Sun L, Huang X H, Zhao F, Zhou N, Chen Z Y, Zhang Q, Yang M, Yang Y C, Guo X X, Li Z B, Liu J, Wang J F, Wang J F, Wang B M, Zhang G L, Sun B C, Zhang W, Kong D L, Chen K X, Li X C. Nat. Commun., 2020, 11: 2961.
[65]
Gehrung M, Crispin-Ortuzar M, Berman A G, O’Donovan M, Fitzgerald R C, Markowetz F. Nat. Med., 2021, 27(5): 833.

doi: 10.1038/s41591-021-01287-9     pmid: 33859411
[66]
Qian X J, Pei J, Zheng H, Xie X X, Yan L, Zhang H, Han C G, Gao X, Zhang H Q, Zheng W W, Sun Q, Lu L, Shung K K. Nat. Biomed. Eng., 2021, 5(6): 522.
[67]
Liu Y, Jain A, Eng C, Way D H, Lee K, Bui P, Kanada K, de Oliveira Marinho G, Gallegos J, Gabriele S, Gupta V, Singh N, Natarajan V, Hofmann-Wellenhof R, Corrado G S, Peng L H, Webster D R, Ai D, Huang S J, Liu Y, Dunn R C, Coz D. Nat. Med., 2020, 26(6): 900.
[68]
Yu G, Sun K, Xu C, Shi X H, Wu C, Xie T, Meng R Q, Meng X H, Wang K S, Xiao H M, Deng H W. Nat. Commun., 2021, 12: 6311.
[1] 丁俊杰 丁晓琴 赵立峰 陈冀胜. 多肽定量构效关系与分子设计[J]. 化学进展, 2005, 17(01): 130-136.
[2] 许禄,胡昌玉. 化学中的人工神经网络法[J]. 化学进展, 2000, 12(01): 18-.