人工智能在乳腺影像领域的应用现状
摘要
乳腺癌在全球范围内发病率高,而乳腺癌的早期发现在很大程度上能提高生存率,改善预后。随着现在计算机存储、运算能力的提升和医学影像大数据的发展,特别是深度学习在医学中的应用,人工智能越来越广泛用于医学影像领域。本文就人工智能在乳腺影像领域的应用现状进行综述。
引文
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[35]Gillies RJ,Kinahan PE,Hricak H.Radiomics:images are more than pictures,they are data[J].Radiology,2016,278(2):563-577.
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[2]Chen W,Zheng R,Baade PD,et al.Cancer statistics in China,2015[J].CA Cancer J Clin,2016,66(2):115-132.
[3]Mendelson EB.Artificial intelligence in breast imaging:potentials and limitations[J].AJR,2019,212(2):293-299.
[4]萧毅,刘士远.客观看待人工智能在医学影像中的作用[J].放射学实践,2018,33(10):992-994.
[5]Jha S,Topol EJ.Adapting to artificial intelligence:radiologists and pathologists as information specialists[J].JAMA,2016,316(22):2353-2354.
[6]Pasa F,Golkov V,Pfeiffer F,et al.Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization[J].Sci Rep,2019,9(1):6268.
[7]Liu Y,Zhang C,Cheng J,et al.A multi-scale data fusion framework for bone age assessment with convolutional neural networks[J].Comput Biol Med,2019,108:161-173.
[8]Song J,Chai YJ,Masuoka H,et al.Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules[J].Medicine,2019,98(15):e15133.
[9]Schmauch B,Herent P,Jehanno P,et al.Diagnosis of focal liver lesions from ultrasound using deep learning[J].Diagn Interv Imaging,2019,100(4):227-233.
[10]Chen G,Zhang J,Zhuo D,et al.Identification of pulmonary noudles via CT images with hierarchical fully convolutional networks[J].Med Biol Eng Comput,2019.In press.DOI:10.1007/s11517-019-01976-1.
[11]Cho J,Park KS,Karki M,et al.Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models[J].J Digit Imaging,2019.In press.DOI:10.1007/s10278-018-00172-1.
[12]S Tandel G,Biswas M,G Kakde O,et al.A review on a deep learning perspective in brain cancer classification[J].Cancers,2019,11(1):E111.
[13]Ahammed Muneer KV,Rajendran VR,K PJ.Glioma tumor grade identification using artificial intelligent techniques[J].JMed Syst,2019,43(5):113.
[14]Mohamed AA,Berg WA,Peng H,et al.A deep learning method for classifying mammographic breast density categories[J].Med Phys,2018,45(1):314-321.
[15]Rodríguez-Ruiz A,Krupinski E,Mordang JJ,et al.Detection of breast cancer with mammography:effect of an artificial intelligence support system[J].Radiology,2019,290(2):305-314.
[16]Samala RK,Chan HP,Hadjiiski L,et al.Mass detection in digital breast tomosynthesis:deep convolutional neural network with transfer learning from mammography[J].Med Phys,2016,43(12):6654-6666.
[17]Qi X,Zhang L,Chen Y,et al.Automated diagnosis of breast ultrasonography images using deep neural networks[J].Med Image Anal,2019,52:185-198.
[18]Zhou J,Luo L,Dou Q,et al.Weakly supervised 3Ddeep learning for breast cancer classification and localization of the lesions in MR images[J].J Magn Reson Imaging,2019.In press.DOI:10.1002/jmri.26721.
[19]Kahn CE Jr.From Images to actions:opportunities for artificial intelligence in radiology[J].Radiology,2017,285(3):719-720.
[20]Topol EJ.High-performance medicine:the convergence of human and artificial intelligence[J].Nat Med,2019,25(1):44-56.
[21]Becker AS,Mueller M,Stoffel E,et al.Classification of breast cancer in ultrasound imaging using ageneric deep learning analysis software:apilot study[J].Br J Radiol,2018,91(1083):20170576.
[22]Wang J,Yang X,Cai H,et al.Discrimination of breast cancer with microcalcifications on mammography by deep learning[J].Sci Rep,2016,6:27327.
[23]Chougrad H,Zouaki H,Alheyane O.Deep convolutional neural networks for breast cancer screening[J].Comput Methods Programs Biomed,2018,157:19-30.
[24]Han S,Kang HK,Jeong JY,et al.A deep learning framework for supporting the classification of breast lesions in ultrasound images[J].Phys Med Biol,2017,62(19):7714-7728.
[25]Huang Y,Han L,Dou H,et al.Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images[J].Biomed Eng Online,2019,18(1):8.
[26]Herent P,Schmauch B,Jehanno P,et al.Detection and characterization of MRI breast lesions using deep learning[J].Diagn Interv Imaging,2019,100(4):219-225.
[27]Ha R,Mutasa S,Karcich J,et al.Predicting breast cancer molecular subtype with MRI dataset utilizing convolutional neural network algorithm[J].J Digit Imaging,2019,32(2):276-282.
[28]Men K,Zhang T,Chen X,et al.Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning[J].Phys Med,2018,50:13-19.
[29]Ha R,Chin C,Karcich J,et al.Prior to initiation of chemotherapy,can we predict breast tumor response?Deep learning convolutional neural networks approach using a breast MRI tumor dataset[J].J Digit Imaging,2018:1-9.DOI:10.1007/s 10278-018-0144-1.
[30]Wang J,Ding H,Bidqoli FA,et al.Detecting cardiovascular disease from mammograms with deep learning[J].IEEE Trans Med Imaging,2017,36(5):1172-1181.
[31]王霄英.人工智能在医学影像中的进展-2017年RSNA参会感受[J].放射学实践,2018,33(2):101-103.
[32]刘士远,萧毅.基于深度学习的人工智能对医学影像学的挑战和机遇[J].中华放射学杂志,2017,51(12):899-901.
[33]中国政府网.国务院:印发《新一代人工智能发展规划》[J].天津中德应用技术大学学报,2017,5(3):7-8.
[34]Syed AB,Zoga AC.Artificial intelligence in radiology:current technology and future directions[J].Semin Musculoskelet Radiol,2018,22(5):540-545.
[35]Gillies RJ,Kinahan PE,Hricak H.Radiomics:images are more than pictures,they are data[J].Radiology,2016,278(2):563-577.
[36]Golden JA.Deep learning algorithms for detection of lymph node metastases from breast cancer:helping artificial intelligence be seen[J].JAMA,2017,318(22):2184-2186.
[37]King BF Jr.Artificial intelligence and radiology:what will the future hold[J].J Am Coll Radiol,2018,15(3):501-503.