基于模式識(shí)別的生物醫(yī)學(xué)圖像處理研究現(xiàn)狀

徐瑩瑩; 沈紅斌

doi:10.11999/JEIT190657

基于模式識(shí)別的生物醫(yī)學(xué)圖像處理研究現(xiàn)狀

doi: 10.11999/JEIT190657 cstr: 32379.14.JEIT190657

徐瑩瑩^{1, 2},
沈紅斌^2, ,

1.
南方醫(yī)科大學(xué)生物醫(yī)學(xué)工程學(xué)院廣州 510515
2.
上海交通大學(xué)圖像處理與模式識(shí)別研究所上海 200240

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61803196, 61671288)，廣東省自然科學(xué)基金(2018030310282)

詳細(xì)信息

作者簡(jiǎn)介:
徐瑩瑩：女，1989年生，副教授，研究方向?yàn)樯飯D像信息學(xué)與模式識(shí)別

沈紅斌：男，1979年生，教授，研究方向?yàn)槟Ｊ阶R(shí)別、數(shù)據(jù)挖掘以及生物信息學(xué)

通訊作者:
沈紅斌　hbshen@sjtu.edu.cn

中圖分類號(hào): TN911.73
計(jì)量
- 文章訪問(wèn)數(shù): 5080
- HTML全文瀏覽量: 3511
- PDF下載量: 544
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-08-29
- 修回日期: 2019-11-12
- 網(wǎng)絡(luò)出版日期: 2019-11-18
- 刊出日期: 2020-01-21

Review of Research on Biomedical Image Processing Based on Pattern Recognition

Yingying XU^{1, 2},
Hongbin SHEN^{2
, ,}

1.
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
2.
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China

Funds: The National Natural Science Foundation of China (61803196, 61671288), The Natural Science Foundation of Guangdong Province (2018030310282)

摘要

摘要:
海量的生物醫(yī)學(xué)圖像蘊(yùn)含著豐富的信息，模式識(shí)別算法能夠從中挖掘規(guī)律并指導(dǎo)生物醫(yī)學(xué)基礎(chǔ)研究和臨床應(yīng)用。近年來(lái)，模式識(shí)別和機(jī)器學(xué)習(xí)理論和實(shí)踐不斷完善，尤其是深度學(xué)習(xí)的廣泛研究和應(yīng)用，促使人工智能、模式識(shí)別與生物醫(yī)學(xué)的交叉研究成為了當(dāng)前的前沿?zé)狳c(diǎn)，相關(guān)的生物醫(yī)學(xué)圖像研究有了突破式的進(jìn)展。該文首先簡(jiǎn)述模式識(shí)別的常用算法，然后總結(jié)了這些算法應(yīng)用于熒光顯微圖像、組織病理圖像、醫(yī)療影像等多種圖像中的挑戰(zhàn)性和國(guó)內(nèi)外研究現(xiàn)狀，最后對(duì)幾個(gè)潛在研究方向進(jìn)行了分析和展望。
- 圖像處理 /
- 生物醫(yī)學(xué)圖像 /
- 模式識(shí)別 /
- 深度學(xué)習(xí)
Abstract:
Pattern recognition algorithms can discover valuable information from mass data of biomedical images as guide for basic research and clinical application. In recent years, with improvement of the theory and practice of pattern recognition and machine learning, especially the appearance and application of deep learning, the crossing researches among artificial intelligence, pattern recognition, and biomedicine become a hotspot, and achieve many breakthrough successes in related fields. This review introduces briefly the common framework and algorithms of image pattern recognition, summarizes the applications of these algorithms to biomedical image analysis including fluorescence microscopic images, histopathological images, and medical radiological images, and finally analyzes and prospect several potential research directions.
- Image processing /
- Biomedical images /
- Pattern recognition /
- Deep learning

HTML全文

圖 1 多種生物醫(yī)學(xué)圖像的示例及在臨床和研究中的主要應(yīng)用

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圖 2 傳統(tǒng)模式識(shí)別方法處理生物醫(yī)學(xué)圖像的一般步驟

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圖 3 經(jīng)典卷積神經(jīng)網(wǎng)絡(luò)模型的時(shí)間軸及特點(diǎn)

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圖 4 熒光點(diǎn)的兩種建模方法示意圖

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圖 5 當(dāng)前生物醫(yī)學(xué)圖像研究中的主要挑戰(zhàn)和可行解決方向

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表 1 常用的生物圖像數(shù)據(jù)集

類型	數(shù)據(jù)集	數(shù)據(jù)量	特點(diǎn)
熒光顯微圖像	CYCLoPs^[23]	超3×10⁵幅蛋白熒光圖像	標(biāo)注酵母細(xì)胞中蛋白質(zhì)16類亞細(xì)胞位置及表達(dá)量
	HPA IF^[24]	2.2×10⁵幅IF圖像	20余個(gè)細(xì)胞系的蛋白圖像，標(biāo)注34類亞細(xì)胞位置
	2D HeLa^[25]	862幅熒光顯微圖像	HeLa宮頸癌細(xì)胞系，標(biāo)注10個(gè)標(biāo)志蛋白的表達(dá)模式
	2D CHO^[26]	327幅熒光顯微圖像	中國(guó)倉(cāng)鼠卵巢細(xì)胞圖像，標(biāo)注5個(gè)標(biāo)志蛋白的表達(dá)模式
組織病理圖像	BreakHis^[27]	7909幅H&E圖像	乳腺良性和惡性腫瘤圖像，共8類病理狀態(tài)
	TCGA^[28]	18462幅H&E圖像	記錄36類癌癥的病理檢查及治療數(shù)據(jù)，
	TMAD^[29]	3726幅IHC圖像	對(duì)蛋白質(zhì)著色的評(píng)分，分為4個(gè)等級(jí)
	HPA IHC^[30]	約10⁶幅IHC圖像	人體正常和癌癥組織的蛋白圖像，標(biāo)注3類亞細(xì)胞位置
醫(yī)療影像圖像	BRATS^[31]	65幅MRI圖像	經(jīng)專家人工分割的腦膠質(zhì)瘤患者的多對(duì)比度MR掃描圖像，兩組癌癥分級(jí)
	ADNI^[32]	2000余名志愿者的MRI、PET圖像	阿爾茨海默病患者和健康組對(duì)照
	ISLES^[33]	103位病人的MRI圖像	缺血性中風(fēng)病人圖像，由專家人工分割出損傷的腦組織
	DeepLesion^[34]	32735幅CT圖像	腎臟病變、骨病變、肺結(jié)節(jié)、淋巴結(jié)腫大等多種病理診斷

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表 2 常用的生物圖像處理工具

類型	處理工具	作用
通用	ImageJ^[36]	對(duì)多種生物醫(yī)學(xué)圖像做如縮放、旋轉(zhuǎn)、平滑、區(qū)域分割、像素統(tǒng)計(jì)等多種處理分析
通用	CellProfiler^[37]	分割熒光點(diǎn)或細(xì)胞，提取細(xì)胞的統(tǒng)計(jì)學(xué)特征
熒光顯微圖像	Squassh^[38]	分割和定量亞細(xì)胞結(jié)構(gòu)
	DeepLoc^[39]	基于熒光圖像預(yù)測(cè)蛋白質(zhì)的亞細(xì)胞位置
	CellOrganizer^[40]	對(duì)多種細(xì)胞亞結(jié)構(gòu)建立生成式模型，產(chǎn)生新的細(xì)胞圖像或視頻
	OMERO.searcher^[41]	圖像匹配和檢索
組織病理圖像	HistomicsML^[42]	交互式機(jī)器學(xué)習(xí)系統(tǒng)，訓(xùn)練基于病理圖像的分類器
	IHC Profiler^[43]	IHC圖像統(tǒng)計(jì)學(xué)特征提取，著色評(píng)分
	iLocator^[44-46]	基于IHC圖像的蛋白質(zhì)亞細(xì)胞位置預(yù)測(cè)系統(tǒng)
醫(yī)療影像圖像	RayPlus^[47]	在線的云端的智能醫(yī)學(xué)影像平臺(tái)，集成3維影像重建、?？朴跋穹治龅裙δ?/td>
	Mimics^[48]	一套高度整合而且易用的3D圖像生成及編輯處理軟件
	ANTS^[49]	提供了高級(jí)的工具用于大腦圖像配準(zhǔn)映射，在解釋和可視化多維數(shù)據(jù)方面有優(yōu)勢(shì)
	FSL^[50]	用于分析fMRI，MRI和DTI大腦成像數(shù)據(jù)的綜合軟件庫(kù)

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參考文獻(xiàn)(100)

MEIJERING E, CARPENTER A E, PENG Hanchuan, et al. Imagining the future of bioimage analysis[J]. Nature Biotechnology, 2016, 34(12): 1250–1255. doi: 10.1038/nbt.3722

江貴平, 秦文健, 周壽軍, 等. 醫(yī)學(xué)圖像分割及其發(fā)展現(xiàn)狀[J]. 計(jì)算機(jī)學(xué)報(bào), 2015, 38(6): 1222–1242. doi: 10.11897/SP.J.1016.2015.01222

JIANG Guiping, QIN Wenjian, ZHOU Shoujun, et al. State-of-the-Art in medical image segmentation[J]. Chinese Journal of Computers, 2015, 38(6): 1222–1242. doi: 10.11897/SP.J.1016.2015.01222

廖苗, 趙于前, 曾業(yè)戰(zhàn), 等. 基于圖割和邊緣行進(jìn)的肝臟CT序列圖像分割[J]. 電子與信息學(xué)報(bào), 2016, 38(6): 1552–1556. doi: 10.11999/JEIT151005

LIAO Miao, ZHAO Yuqian, ZENG Yezhan, et al. Liver segmentation from abdominal CT volumes based on graph cuts and border marching[J]. Journal of Electronics &Information Technology, 2016, 38(6): 1552–1556. doi: 10.11999/JEIT151005

文登偉, 張東波, 湯紅忠, 等. 融合紋理與形狀特征的HEp-2細(xì)胞分類[J]. 電子與信息學(xué)報(bào), 2017, 39(7): 1599–1605. doi: 10.11999/JEIT161090

WEN Dengwei, ZHANG Dongbo, TANG Hongzhong, et al. HEp-2 cell classification by fusing texture and shape features[J]. Journal of Electronics &Information Technology, 2017, 39(7): 1599–1605. doi: 10.11999/JEIT161090

田娟秀, 劉國(guó)才, 谷珊珊, 等. 醫(yī)學(xué)圖像分析深度學(xué)習(xí)方法研究與挑戰(zhàn)[J]. 自動(dòng)化學(xué)報(bào), 2018, 44(3): 401–424. doi: 10.16383/j.aas.2018.c170153

TIAN Juanxiu, LIU Guocai, GU Shanshan, et al. Deep learning in medical image analysis and its challenges[J]. Acta Automatica Sinica, 2018, 44(3): 401–424. doi: 10.16383/j.aas.2018.c170153

DE MATOS J, DE SOUZA BRITTO JR A, OLIVEIRA L E S, et al. Histopathologic image processing: A review[J]. arXiv: 1904.07900, 2019.

XU Yingying, YAO Lixiu, and SHEN Hongbin. Bioimage-based protein subcellular location prediction: A comprehensive review[J]. Frontiers of Computer Science, 2018, 12(1): 26–39. doi: 10.1007/s11704-016-6309-5

LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis[J]. Medical Image Analysis, 2017, 42: 60–88. doi: 10.1016/j.media.2017.07.005

KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. Imagenet classification with deep convolutional neural networks[C]. The 25th International Conference on Neural Information Processing Systems, Lake Taboe, USA, 2012: 1097–1105.

AWAD A I and HASSABALLAH M. Image Feature Detectors and Descriptors: Foundations and Applications[M]. Cham: Springer International Publishing, 2016. doi: 10.1007/978-3-319-28854-3.

LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278–2324. doi: 10.1109/5.726791

SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv: 1409.1556, 2014.

SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 1–9.

HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.

LONG J, SHELHAMER E, and DARRELL T. Fully convolutional networks for semantic segmentation[C]. 2015 IEEE Computer Vision and Pattern Recognition, Boston, USA, 2015: 3431–3440.

SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning[C]. The 31st AAAI Conference on Artificial Intelligence, San Francisco, 2017: 4278–4284.

HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]. The 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 4700–4708.

IANDOLA F N, HAN Song, MOSKEWICZ M W, et al. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5 MB model size[J]. arXiv: 1602.07360, 2016.

HOWARD A G, ZHU Menglong, CHEN Bo, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv: 1704.04861, 2017.

TAN Mingxing and LE Q V. Efficientnet: Rethinking model scaling for convolutional neural networks[J]. arXiv: 1905.11946, 2019.

CIOMPI F, DE HOOP B, VAN RIEL S J, et al. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box[J]. Medical Image Analysis, 2015, 26(1): 195–202. doi: 10.1016/j.media.2015.08.001

KHOSRAVI P, KAZEMI E, IMIELINSKI M, et al. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images[J]. EBioMedicine, 2018, 27: 317–328. doi: 10.1016/j.ebiom.2017.12.026

KOH J L Y, CHONG Y T, FRIESEN H, et al. Cyclops: A comprehensive database constructed from automated analysis of protein abundance and subcellular localization patterns in saccharomyces cerevisiae[J]. G3: Genes, Genomes, Genetics, 2015, 5(6): 1223–1232. doi: 10.1534/g3.115.017830

THUL P J, ?KESSON L, WIKING M, et al. A subcellular map of the human proteome[J]. Science, 2017, 356(6340): eaal3321. doi: 10.1126/science.aal3321

BOLAND M V and MURPHY R F. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of hela cells[J]. Bioinformatics, 2001, 17(12): 1213–1223. doi: 10.1093/bioinformatics/17.12.1213

彭佳林, 揭萍. 基于序列間先驗(yàn)約束和多視角信息融合的肝臟CT圖像分割[J]. 電子與信息學(xué)報(bào), 2018, 40(4): 971–978. doi: 10.11999/JEIT170933

PENG Jialin and JIE Ping. Liver segmentation from CT image based on sequential constraint and multi-view information fusion[J]. Journal of Electronics &Information Technology, 2018, 40(4): 971–978. doi: 10.11999/JEIT170933

SPANHOL F A, OLIVEIRA L S, PETITJEAN C, et al. A dataset for breast cancer histopathological image classification[J]. IEEE Transactions on Biomedical Engineering, 2016, 63(7): 1455–1462. doi: 10.1109/TBME.2015.2496264

The Cancer Genome Atlas Research Network, WEINSTEIN J N, COLLISSON E A, et al. The cancer genome atlas pan-cancer analysis project[J]. Nature Genetics, 2015, 45(10): 1113–1120. doi: 10.1038/ng.2764

MARINELLI R J, MONTGOMERY K, LIU C L, et al. The stanford tissue microarray database[J]. Nucleic Acids Research, 2008, 36(S1): D871–D877. doi: 10.1093/nar/gkm861

UHLéN M, FAGERBERG L, HALLSTR?M B M, et al. Tissue-based map of the human proteome[J]. Science, 2015, 347(6220): 1260419. doi: 10.1126/science.1260419

MENZE B H, JAKAB A, BAUER S, et al. The multimodal brain tumor image segmentation benchmark (brats)[J]. IEEE Transactions on Medical Imaging, 2015, 34(10): 1993–2024. doi: 10.1109/TMI.2014.2377694

IWATSUBO T. Alzheimer's disease neuroimaging initiative (ADNI)[J]. Nihon Rinsho Japanese Journal of Clinical Medicine, 2011, 69(S8): 570–574.

MAIER O, MENZE B H, VON DER GABLENTZ J, et al. ISLES 2015 - a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral mri[J]. Medical Image Analysis, 2017, 35: 250–269. doi: 10.1016/j.media.2016.07.009

YAN Ke, WANG Xiaosong, LU Le, et al. Deeplesion: Automated mining of large-scale lesion annotations and universal lesion detection with deep learning[J]. Journal of Medical Imaging, 2018, 5(3): 036501. doi: 10.1117/1.JMI.5.3.036501

RONNEBERGER O, FISCHER P, and BROX T. U-net: Convolutional networks for biomedical image segmentation[C]. The 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234–241.

SCHINDELIN J, RUEDEN C T, HINER M C, et al. The imagej ecosystem: An open platform for biomedical image analysis[J]. Molecular Reproduction & Development, 2015, 82(7/8): 518–529. doi: 10.1002/mrd.22489

CARPENTER A E, JONES T R, LAMPRECHT M R, et al. Cellprofiler: Image analysis software for identifying and quantifying cell phenotypes[J]. Genome Biology, 2006, 7(10): R100. doi: 10.1186/gb-2006-7-10-r100

RIZK A, PAUL G, INCARDONA P, et al. Segmentation and quantification of subcellular structures in fluorescence microscopy images using squassh[J]. Nature Protocols, 2014, 9(3): 586–596. doi: 10.1038/nprot.2014.037

KRAUS O Z, GRYS B T, BA J, et al. Automated analysis of high-content microscopy data with deep learning[J]. Molecular Systems Biology, 2017, 13(4): 924. doi: 10.15252/msb.20177551

MURPHY R F. Cellorganizer: Image-derived models of subcellular organization and protein distribution[J]. Methods in Cell Biology, 2012, 110: 179–193. doi: 10.1016/B978-0-12-388403-9.00007-2

CHO B H, CAO-BERG I, BAKAL J A, et al. OMERO.searcher: Content-based image search for microscope images[J]. Nature Methods, 2012, 9(7): 633–634. doi: 10.1038/nmeth.2086

NALISNIK M, AMGAD M, LEE S, et al. Interactive phenotyping of large-scale histology imaging data with HistomicsML[J]. Scientific Reports, 2017, 7(1): 14588. doi: 10.1038/s41598-017-15092-3

VARGHESE F, BUKHARI A B, MALHOTRA R, et al. Ihc profiler: An open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples[J]. PLoS One, 2014, 9(5): e96801. doi: 10.1371/journal.pone.0096801

XU Yingying, YANG Fan, and SHEN Hongbin. Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction[J]. Bioinformatics, 2016, 32(14): 2184–2192. doi: 10.1093/bioinformatics/btw219

XU Yingying, YANG Fan, ZHANG Yang, et al. An image-based multi-label human protein subcellular localization predictor (ilocator) reveals protein mislocalizations in cancer tissues[J]. Bioinformatics, 2013, 29(16): 2032–2040. doi: 10.1093/bioinformatics/btt320

XU Yingying, YANG Fan, ZHANG Yang, et al. Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning[J]. Bioinformatics, 2015, 31(7): 1111–1119. doi: 10.1093/bioinformatics/btu772

YUAN Rong, LUO Ming, SUN Zhi, et al. Rayplus: A web-based platform for medical image processing[J]. Journal of Digital Imaging, 2017, 30(2): 197–203. doi: 10.1007/s10278-016-9920-y

MIMICS.[M/OL]. Https: //www.Materialise.Com/en/medical/software/mimics, 2019.

AVANTS B B, TUSTISON N, and SONG Gang. Advanced normalization tools (ANTS)[R]. Pennsylvania: University of Pennsylvania, 2011: 1–35.

SMITH S M, JENKINSON M, WOOLRICH M W, et al. Advances in functional and structural mr image analysis and implementation as fsl[J]. Neuroimage, 2004, 23(S1): S208–S219. doi: 10.1016/j.neuroimage.2004.07.051

LOO L H, LAKSAMEETHANASAN D, and TUNG Y L. Quantitative protein localization signatures reveal an association between spatial and functional divergences of proteins[J]. PLoS Computational Biology, 2014, 10(3): e1003504. doi: 10.1371/journal.pcbi.1003504

LU A X, CHONG Y T, HSU I S, et al. Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins[J]. eLife, 2018, 7: e31872. doi: 10.7554/eLife.31872

LI Jieyue, SHARIFF A, WIKING M, et al. Estimating microtubule distributions from 2d immunofluorescence microscopy images reveals differences among human cultured cell lines[J]. PLoS One, 2012, 7(11): e50292. doi: 10.1371/journal.pone.0050292

NANNI L and MELUCCI M. Combination of projectors, standard texture descriptors and bag of features for classifying images[J]. Neurocomputing, 2016, 173: 1602–1614. doi: 10.1016/j.neucom.2015.09.032

COELHO L P, KANGAS J D, NAIK A W, et al. Determining the subcellular location of new proteins from microscope images using local features[J]. Bioinformatics, 2013, 29(18): 2343–2349. doi: 10.1093/bioinformatics/btt392

P?RNAMAA T and PARTS L. Accurate classification of protein subcellular localization from high-throughput microscopy images using deep learning[J]. G3: Genes, Genomes, Genetics, 2017, 7(5): 1385–1392. doi: 10.1534/g3.116.033654

SULLIVAN D P, WINSNES C F, ?KESSON L, et al. Deep learning is combined with massive-scale citizen science to improve large-scale image classification[J]. Nature Biotechnology, 2018, 36(9): 820–828. doi: 10.1038/nbt.4225

RUMETSHOFER E, HOFMARCHER M, R?HRL C, et al. Human-level protein localization with convolutional neural networks[C]. The ICLR 2019, Las Cruces, USA, 2019: 1–18.

CHEN Jiamei, QU Aiping, WANG Linwei, et al. New breast cancer prognostic factors identified by computer-aided image analysis of he stained histopathology images[J]. Scientific Reports, 2015, 5: 10690. doi: 10.1038/srep10690

SANTAMARIA-PANG A, RITTSCHER J, GERDES M, et al. Cell segmentation and classification by hierarchical supervised shape ranking[C]. The 12th IEEE International Symposium on Biomedical Imaging, New York, USA, 2015: 1296–1299.

GEESSINK O G F, BAIDOSHVILI A, FRELING G, et al. Toward automatic segmentation and quantification of tumor and stroma in whole-slide images of h and e stained rectal carcinomas[J]. SPIE, 2015, 9420: 94200F. doi: 10.1117/12.2081665.

ARTETA C, LEMPITSKY V, NOBLE J A, et al. Learning to detect cells using non-overlapping extremal regions[C]. The 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, Nice, France, 2012: 348–356.

QU Aiping, CHEN Jiamei, WANG Linwei, et al. Two-step segmentation of hematoxylin-eosin stained histopathological images for prognosis of breast cancer[C]. 2014 IEEE International Conference on Bioinformatics and Biomedicine, Belfast, UK, 2014: 218–223.

BEJNORDI B E, LIN J, GLASS B, et al. Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images[C]. The 14th IEEE International Symposium on Biomedical Imaging, Melbourne, Australia, 2017: 929–932. doi: 10.1109/ISBI.2017.7950668.

SIRINUKUNWATTANA K, SNEAD D R J, and RAJPOOT N M. A stochastic polygons model for glandular structures in colon histology images[J]. IEEE Transactions on Medical Imaging, 2015, 34(11): 2366–2378. doi: 10.1109/TMI.2015.2433900

XU Yan, LI Yang, WANG Yipei, et al. Gland instance segmentation using deep multichannel neural networks[J]. IEEE Transactions on Biomedical Engineering, 2017, 64(12): 2901–2912. doi: 10.1109/TBME.2017.2686418

XU Yan, JIA Zhipeng, WANG Liangbo, et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features[J]. BMC Bioinformatics, 2017, 18(1): 281. doi: 10.1186/s12859-017-1685-x

BARKER J, HOOGI A, DEPEURSINGE A, et al. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles[J]. Medical Image Analysis, 2016, 30: 60–71. doi: 10.1016/j.media.2015.12.002

BENTAIEB A, LI-CHANG H, HUNTSMAN D, et al. A structured latent model for ovarian carcinoma subtyping from histopathology slides[J]. Medical Image Analysis, 2017, 39: 194–205. doi: 10.1016/j.media.2017.04.008

YU K H, ZHANG Ce, BERRY G J, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features[J]. Nature Communications, 2016, 7(1): 12474. doi: 10.1038/ncomms12474

CHENG Jun, ZHANG Jie, HAN Yatong, et al. Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis[J]. Cancer Research, 2017, 77(21): e91–e100. doi: 10.1158/0008-5472.CAN-17-0313

SHARMA H, ZERBE N, KLEMPERT I, et al. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology[J]. Computerized Medical Imaging and Graphics, 2017, 61: 2–13. doi: 10.1016/j.compmedimag.2017.06.001

BENTAIEB A, LI-CHANG H, HUNTSMAN D, et al. Automatic diagnosis of ovarian carcinomas via sparse multiresolution tissue representation[C]. The 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 629–636. doi: 10.1007/978-3-319-24553-9_77.

WANG Chaofeng, SHI Jun, ZHANG Qi, et al. Histopathological image classification with bilinear convolutional neural networks[C]. The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Seogwipo, South Korea, 2017: 4050–4053. doi: 10.1109/EMBC.2017.8037745.

CIOMPI F, GEESSINK O, BEJNORDI B E, et al. The importance of stain normalization in colorectal tissue classification with convolutional networks[C]. The 14th IEEE International Symposium on Biomedical Imaging, Melbourne, Australia, 2017: 160–163. doi: 10.1109/ISBI.2017.7950492.

SHEIKHZADEH F, WARD R K, VAN NIEKERK D, et al. Automatic labeling of molecular biomarkers of immunohistochemistry images using fully convolutional networks[J]. PLoS One, 2018, 13(1): e0190783. doi: 10.1371/journal.pone.0190783

COUDRAY N, OCAMPO P S, SAKELLAROPOULOS T, et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning[J]. Nature Medicine, 2018, 24(10): 1559–1567. doi: 10.1038/s41591-018-0177-5

KWAK J T and HEWITT S M. Nuclear architecture analysis of prostate cancer via convolutional neural networks[J]. IEEE Access, 2017, 5: 18526–18533. doi: 10.1109/ACCESS.2017.2747838

OIKAWA K, SAITO A, KIYUNA T, et al. Pathological diagnosis of gastric cancers with a novel computerized analysis system[J]. Journal of Pathology Informatics, 2017, 8(1): 5. doi: 10.4103/2153-3539.201114

QAISER T, MUKHERJEE A, REDDY P B C, et al. Her2 challenge contest: A detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues[J]. Histopathology, 2018, 72(2): 227–238. doi: 10.1111/his.13333

NEWBERG J and MURPHY R F. A framework for the automated analysis of subcellular patterns in human protein atlas images[J]. Journal of Proteome Research, 2008, 7(6): 2300–2308. doi: 10.1021/pr7007626

KUMAR A, RAO A, BHAVANI S, et al. Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(51): 18249–18254. doi: 10.1073/pnas.1415120112

KONDO S, TAKAGI K, NISHIDA M, et al. Computer-aided diagnosis of focal liver lesions using contrast-enhanced ultrasonography with perflubutane microbubbles[J]. IEEE Transactions on Medical Imaging, 2017, 36(7): 1427–1437. doi: 10.1109/TMI.2017.2659734

KUPPILI V, BISWAS M, SREEKUMAR A, et al. Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization[J]. Journal of Medical Systems, 2017, 41(10): 152. doi: 10.1007/s10916-017-0862-9

SERAG A, WILKINSON A G, TELFORD E J, et al. Segma: An automatic segmentation approach for human brain mri using sliding window and random forests[J]. Frontiers in Neuroinformatics, 2017, 11: 2. doi: 10.3389/fninf.2017.00002

KHAN S A, NAZIR M, KHAN M A, et al. Lungs nodule detection framework from computed tomography images using support vector machine[J]. Microscopy Research & Technique, 2019, 82(8): 1256–1266. doi: 10.1002/jemt.23275

TIZHOOSH H R and BABAIE M. Representing medical images with encoded local projections[J]. IEEE Transactions on Biomedical Engineering, 2018, 65(10): 2267–2277. doi: 10.1109/TBME.2018.2791567

GAO X W, HUI Rui, and TIAN Zengmin. Classification of ct brain images based on deep learning networks[J]. Computer Methods and Programs in Biomedicine, 2017, 138: 49–56. doi: 10.1016/j.cmpb.2016.10.007

VAN TULDER G and DE BRUIJNE M. Combining generative and discriminative representation learning for lung ct analysis with convolutional restricted boltzmann machines[J]. IEEE Transactions on Medical Imaging, 2016, 35(5): 1262–1272. doi: 10.1109/TMI.2016.2526687

TERAMOTO A, FUJITA H, YAMAMURO O, et al. Automated detection of pulmonary nodules in pet/ct images: Ensemble false-positive reduction using a convolutional neural network technique[J]. Medical Physics, 2016, 43(6): 2821–2827. doi: 10.1118/1.4948498

CHEN Hao, WU Lingyun, DOU Qi, et al. Ultrasound standard plane detection using a composite neural network framework[J]. IEEE Transactions on Cybernetics, 2017, 47(6): 1576–1586. doi: 10.1109/TCYB.2017.2685080

AREVALO J, GONZáLEZ F A, RAMOS-POLLáN R, et al. Representation learning for mammography mass lesion classification with convolutional neural networks[J]. Computer Methods and Programs in Biomedicine, 2016, 127: 248–257. doi: 10.1016/j.cmpb.2015.12.014

CUI Zhipeng, YANG Jie, and QIAO Yu. Brain MRI segmentation with patch-based CNN approach[C]. The 35th Chinese Control Conference, Chengdu, China, 2016: 7026–7031.

RAJPURKAR P, IRVIN J, ZHU K, et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning[J]. arXiv: 1711.05225, 2017.

CHRISTIANSEN E M, YANG S J, ANDO D M, et al. In silico labeling: Predicting fluorescent labels in unlabeled images[J]. Cell, 2018, 173(3): 792–803. doi: 10.1016/j.cell.2018.03.040

BRENT R and BOUCHERON L E. Deep learning to predict microscope images[J]. Nature Methods, 2018, 15(11): 868–870. doi: 10.1038/s41592-018-0194-9

RIVENSON Y, WANG Hongda, WEI Zhensong, et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning[J]. Nature Biomedical Engineering, 2019, 3(6): 466–477. doi: 10.1038/s41551-019-0362-y

ZHANG Zizhao, CHEN Pingjun, MCGOUGH M, et al. Pathologist-level interpretable whole-slide cancer diagnosis with deep learning[J]. Nature Machine Intelligence, 2019, 1(5): 236–245. doi: 10.1038/s42256-019-0052-1

VAKOC B J, LANNING R M, TYRRELL J A, et al. Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging[J]. Nature Medicine, 2009, 15(10): 1219–1223. doi: 10.1038/nm.1971

JONES S A, SHIM S, HE Jiang, et al. Fast, three-dimensional super-resolution imaging of live cells[J]. Nature Methods, 2011, 8(6): 499–505. doi: 10.1038/nmeth.1605

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