切换至 "中华医学电子期刊资源库"

中华乳腺病杂志(电子版) ›› 2021, Vol. 15 ›› Issue (04) : 238 -241. doi: 10.3877/cma.j.issn.1674-0807.2021.04.009

所属专题: 文献

综述

深度学习在乳腺X线摄影中的应用
邢家诚1, 闫石1, 蔡莉1,()   
  1. 1. 150081 哈尔滨医科大学附属第三医院乳腺内科二病区
  • 收稿日期:2020-09-05 出版日期:2021-09-08
  • 通信作者: 蔡莉

Application of deep learning in mammography

Jiacheng Xing1, Shi Yan1, Li Cai1()   

  • Received:2020-09-05 Published:2021-09-08
  • Corresponding author: Li Cai
引用本文:

邢家诚, 闫石, 蔡莉. 深度学习在乳腺X线摄影中的应用[J/OL]. 中华乳腺病杂志(电子版), 2021, 15(04): 238-241.

Jiacheng Xing, Shi Yan, Li Cai. Application of deep learning in mammography[J/OL]. Chinese Journal of Breast Disease(Electronic Edition), 2021, 15(04): 238-241.

近年来,人工智能在乳腺X线摄影中有着十分广泛的应用。目前,深度学习已经成为人工智能图像处理领域最先进的手段之一,其对肿瘤位置的确定和肿瘤性质的判定已经达到专业影像医师的程度。此外,深度学习还具有其他重要功能,如:构建预测模型,评估患病风险;提取成像特征,降低召回率;合成乳腺X线图像用于教育和科研等。笔者对深度学习的概念及其在乳腺X线摄影中的应用作一综述,供乳腺科和医学影像科医师参考。

图1 机器学习、神经网络、深度学习和卷积神经网络的关系图
图2 深度学习系统的分层结构
[1]
Independent UK Panel on Breast Cancer Screening. The benefits and harms of breast cancer screening: an independent review[J]. Lancet, 2012, 380(9855):1778-1786.
[2]
Moore SG, Shenoy PJ, Fanucchi L, et al. Cost-effectiveness of MRI compared to mammography for breast cancer screening in a high risk population[J]. BMC Health Serv Res, 2009, 9:9.
[3]
Le E, Wang Y, Huang Y, et al. Artificial intelligence in breast imaging[J]. Clin Radiol, 2019, 74(5):357-366.
[4]
Burt JR, Torosdagli N, Khosravan N, et al. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks[J]. Br J Radiol, 2018, 91(1089):20170545.
[5]
Huang GB, Chen L, Siew CK. Universal approximation using incremental constructive feedforward networks with random hidden nodes[J]. IEEE Trans Neural Netw, 2006, 17(4):879-892.
[6]
LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
[7]
Ardila D, Kiraly AP, Bharadwaj S, et al. Author correction: end-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography[J]. Nat Med, 2019, 25(8):1319.
[8]
Tandel GS, Biswas M, Kakde OG, et al. A review on a deep learning perspective in brain cancer classification[J]. Cancers (Basel), 2019, 11(1):111.
[9]
Wang P, Xiao X, Glissen Brown JR, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy[J]. Nat Biomed Eng, 2018, 2(10):741-748.
[10]
Wang CJ, Hamm CA, Savic LJ, et al. Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features[J]. Eur Radiol, 2019, 29(7):3348-3357.
[11]
Lin YC, Lin CH, Lu HY, et al. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer[J]. Eur Radiol, 2020, 30(3):1297-1305.
[12]
Schelb P, Kohl S, Radtke JP, et al. Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment[J]. Radiology, 2019, 293(3):607-617.
[13]
Gastounioti A, Conant EF, Kontos D. Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment[J]. Breast Cancer Res, 2016, 18(1):91.
[14]
Arefan D, Mohamed AA, Berg WA, et al. Deep learning modeling using normal mammograms for predicting breast cancer risk[J]. Med Phys, 2020, 47(1):110-118.
[15]
Sprague BL, Conant EF, Onega T, et al. Variation in mammographic breast density assessments among radiologists in clinical practice: a multicenter observational study[J]. Ann Intern Med, 2016, 165(7):457-464.
[16]
Yala A, Lehman C, Schuster T, et al. A deep learning mammography-based model for improved breast cancer risk prediction[J]. Radiology, 2019, 292(1):60-66.
[17]
Shi B, Grimm LJ, Mazurowski MA, et al. Prediction of occult invasive disease in ductal carcinoma in situ using deep learning features[J]. J Am Coll Radiol, 2018, 15(3 Pt B):527-534.
[18]
Fenton JJ, Taplin SH, Carney PA, et al. Influence of computer-aided detection on performance of screening mammography[J]. N Engl J Med, 2007, 356(14):1399-1409.
[19]
Lehman CD, Wellman RD, Buist DS, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection[J]. JAMA Intern Med, 2015, 175(11):1828-1837.
[20]
Gao Y, Geras KJ, Lewin AA, et al. New frontiers: an update on computer-aided diagnosis for breast imaging in the age of artificial intelligence[J]. AJR Am J Roentgenol, 2019, 212(2):300-307.
[21]
Ribli D, Horváth A, Unger Z, et al. Detecting and classifying lesions in mammograms with deep learning[J]. Sci Rep, 2018, 8(1):4165.
[22]
Sankar D, Thomas T. A new fast fractal modeling approach for the detection of microcalcifications in mammograms[J]. J Digit Imaging, 2010, 23(5):538-546.
[23]
Iacomi M, Cascio D, Fauci F, et al. Mammographic images segmentation based on chaotic map clustering algorithm[J]. BMC Med Imaging, 2014, 14:12.
[24]
Cai H, Huang Q, Rong W, et al. Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms[J]. Comput Math Methods Med, 2019, 2019:2 717 454.
[25]
Wang J, Yang X, Cai H, et al. Discrimination of breast cancer with microcalcifications on mammography by deep learning[J]. Sci Rep, 2016, 6:27 327.
[26]
Gao Y, Babb JS, Toth HK, et al. Digital breast tomosynthesis practice patterns following 2011 FDA approval: a survey of breast imaging radiologists[J]. Acad Radiol, 2017, 24(8):947-953.
[27]
Zhang X, Zhang Y, Han EY, et al. Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks[J]. IEEE Trans Nanobioscience, 2018, 17(3):237-242.
[28]
Lee JG, Jun S, Cho YW, et al. Deep learning in medical imaging: general overview[J]. Korean J Radiol, 2017, 18(4):570-584.
[29]
Samala RK, Chan H, Hadjiiski L, et al. Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets[J]. IEEE Trans Med Imaging, 2019, 38(3):686-696.
[30]
Aboutalib SS, Mohamed AA, Berg WA, et al. Deep learning to distinguish recalled but benign mammography images in breast cancer screening[J]. Clin Cancer Res, 2018, 24(23):5902-5909.
[31]
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017, 42:60-88.
[32]
Mendel K, Li H, Sheth D, et al. Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast tomosynthesis and full-field digital mammography[J]. Acad Radiol, 2019, 26(6):735-743.
[33]
Bart E, Hegdé J. Deep synthesis of realistic medical images: a novel tool in clinical research and training[J]. Front Neuroinform, 2018, 12:82.
[34]
Guan S, Loew M. Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks[J]. J Med Imaging (Bellingham), 2019, 6(3):031 411.
[35]
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare[J]. Nat Med, 201925(1):24-29.
[1] 李洋, 蔡金玉, 党晓智, 常婉英, 巨艳, 高毅, 宋宏萍. 基于深度学习的乳腺超声应变弹性图像生成模型的应用研究[J/OL]. 中华医学超声杂志(电子版), 2024, 21(06): 563-570.
[2] 河北省抗癌协会乳腺癌专业委员会护理协作组. 乳腺癌中心静脉通路护理管理专家共识[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(06): 321-329.
[3] 刘晨鹭, 刘洁, 张帆, 严彩英, 陈倩, 陈双庆. 增强MRI 影像组学特征生境分析在预测乳腺癌HER-2 表达状态中的应用[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(06): 339-345.
[4] 张晓宇, 殷雨来, 张银旭. 阿帕替尼联合新辅助化疗对三阴性乳腺癌的疗效及预后分析[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(06): 346-352.
[5] 邱琳, 刘锦辉, 组木热提·吐尔洪, 马悦心, 冷晓玲. 超声影像组学对致密型乳腺背景中非肿块型乳腺癌的诊断价值[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(06): 353-360.
[6] 程燕妮, 樊菁, 肖瑶, 舒瑞, 明昊, 党晓智, 宋宏萍. 乳腺组织定位标记夹的应用与进展[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(06): 361-365.
[7] 涂盛楠, 胡芬, 张娟, 蔡海峰, 杨俊泉. 天然植物提取物在乳腺癌治疗中的应用[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(06): 366-370.
[8] 朱文婷, 顾鹏, 孙星. 非酒精性脂肪性肝病对乳腺癌发生发展及治疗的影响[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(06): 371-375.
[9] 韩萌萌, 冯雪园, 马宁. 乳腺癌改良根治术后桡神经损伤1例[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 117-118.
[10] 高杰红, 黎平平, 齐婧, 代引海. ETFA和CD34在乳腺癌中的表达及与临床病理参数和预后的关系研究[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 64-67.
[11] 张志兆, 王睿, 郜苹苹, 王成方, 王成, 齐晓伟. DNMT3B与乳腺癌预后的关系及其生物学机制[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 624-629.
[12] 王玲艳, 高春晖, 冯雪园, 崔鑫淼, 刘欢, 赵文明, 张金库. 循环肿瘤细胞在乳腺癌新辅助及术后辅助治疗中的应用[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 630-633.
[13] 赵林娟, 吕婕, 王文胜, 马德茂, 侯涛. 超声引导下染色剂标记切缘的梭柱型和圆柱型保乳区段切除术的效果研究[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 634-637.
[14] 熊鹰, 林敬莱, 白奇, 郭剑明, 王烁. 肾癌自动化病理诊断:AI离临床还有多远?[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 535-540.
[15] 孙铭远, 褚恒, 徐海滨, 张哲. 人工智能应用于多发性肺结节诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 785-790.
阅读次数
全文


摘要