1 |
寿君妮, 于观贞, 余党会, 等. 人工智能与医学——发展历程 [J]. 第二军医大学学报, 2018, 39(8): 806.
|
2 |
Vyborny CJ, Giger ML. Computer vision and artificial intelligence in mammography [J]. AJR Am J Roentgenol, 1994, 162(3): 699-708.
|
3 |
李志勇, 李鹏伟, 高小燕, 等. 人工智能医学技术发展的聚焦领域与趋势分析 [J]. 中国医学装备, 2018, 15(7): 136-145.
|
4 |
Israni ST, Verghese A. Humanizing Artificial Intelligence [J]. JAMA, 2019, 321(1): 29-30.
|
5 |
He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine [J]. Nat Med, 2019, 25(1): 30-36.
|
6 |
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications [J]. CA Cancer J Clin, 2019, 69(2): 127-157.
|
7 |
Coiera E. The fate of medicine in the time of AI [J]. Lancet, 2018, 392(10162): 2331-2332.
|
8 |
National Comprehensive Cancer Network. Breast Cancer [EB/OL].(2019-03-14) [2019-03-19].
URL
|
9 |
National Cancer Institute. Annual Report to the Nation 2018: National Cancer Statistics [EB/OL]. (2018-05-22) [2019-03-19].
URL
|
10 |
Marmot MG, Altman DG, Cameron DA, et al. The benefits and harms of breast cancer screening: an independent review [J]. Br J Cancer, 2013, 108(11): 2205-2240.
|
11 |
Sprague BL, Arao RF, Miglioretti DL, et al. National Performance Benchmarks for Modern Diagnostic Digital, Mammography: Update from the Breast Cancer Surveillance, Consortium [J]. Radiology, 2017, 283(1): 59-69.
|
12 |
余立君. 乳腺癌早期诊断设备及其技术进展——乳腺CAD、电阻抗、电子触诊、激光等技术进展(三) [J]. 中国医疗设备, 2010, 25(9): 54-56.
|
13 |
Marinovich ML, Hunter KE, Macaskill P, et al. Breast Cancer Screening Using Tomosynthesis or Mammography: A Meta-analysis of Cancer Detection and Recall [J]. J Natl Cancer Inst, 2018, 110(9): 942-949.
|
14 |
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.
|
15 |
Chan HP, Lo SC, Sahiner B, et al. Computer-aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network [J]. Med Phys, 1995, 22(10): 1555.
|
16 |
王晓行. 乳腺癌AI辅助诊断系统获得CFDA认证,部分病变检出率高达96% -动脉网[EB/OL]. (2017-05-16) [2019-03-19]
URL
|
17 |
National Comprehensive Cancer Network. Breast Cancer Screening and Diagnosis [EB/OL]. (2018-10-04) [2019-03-19].
URL
|
18 |
Summary of safety and effectiveness [EB/OL ]. (2017-06-05) [2019-03-19].
URL
|
19 |
李俊来, 宋丹绯, 张艳, 等. B-CAD辅助乳腺超声检查诊断乳腺癌的价值 [J]. 中国超声医学杂志, 2009, 25(2): 124-127.
|
20 |
Yap MH, Pons G, Marti J, et al. Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks [J]. IEEE J Biomed Health Inform, 2018, 22(4): 1218-1226.
|
21 |
Drukker K, Sennett CA, Giger ML, et al. Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts [J]. Med Phys, 2013, 41(1): 012901.
|
22 |
van Zelst J, Tan T, Clauser P, et al. Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts [J]. Eur Radiol, 2018, 28(7): 1-11.
|
23 |
Berg WA, Blume JD, Cormack J, et al. Combined Screening With Ultrasound and Mammography vs Mammography Alone in Women at Elevated Risk of Breast Cancer [J]. JAMA, 2008, 299(18): 2151-2163.
|
24 |
Zhang X, Lin X, Tan Y, et al. A multicenter hospital-based diagnosis study of automated breast ultrasound system in detecting breast cancer among Chinese women [J]. Chin J Cancer Res, 2018, 30(2): 231-239.
|
25 |
Xu X, Bao L, Tan Y, et al. 1000-Case Reader Study of Radiologists′ Performance in Interpretation of Automated Breast Volume Scanner Images with a Computer-Aided Detection System [J]. Ultrasound Med Biol, 2018, 44(8): 1694-1702.
|
26 |
Wang N, Bian C, Wang Y, et al. Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound [C]. medical image computing and computer assisted intervention, 2018: 641-648.
|
27 |
Wood C. Computer Aided Detection (CAD) for breast MRI [J]. Technol Cancer Res Treat, 2005, 4(1): 49-53.
|
28 |
Chamming′s F, Ueno Y, Ferré R, et al. Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy [J]. Radiology, 2017, 286(2): 412-420.
|
29 |
Saha A, Harowicz MR, Grimm LJ, et al. A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features [J]. Br J Cancer, 2018, 119(4): 508-516.
|
30 |
Ravdin PM, Siminoff LA, Davis GJ, et al. Computer Program to Assist in Making Decisions About Adjuvant Therapy for Women With Early Breast Cancer [J]. J Clin Oncol, 2001, 19(4): 980-991.
|
31 |
Olivotto IA, Bajdik C, Ravdin PM, et al. Population-Based Validation of the Prognostic Model ADJUVANT! for Early Breast Cancer [J]. J Clin Oncol, 2005, 23(12): 2716-2725.
|
32 |
Giger ML, Deasy JO. MO-FG-207B-00: State-of-the-Art in Radiomics in Radiology and Radiation Oncology [J]. Medical Physics, 2016, 43(6): 3715-3715.
|
33 |
Amin MB, Edge SB, Greene FL, et al. AJCC Cancer Staging Manual. 8th ed [M]. NewYork: Springer, 2017.
|
34 |
Bejnordi BE, Veta M, Van Diest PJ, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer [J]. JAMA, 2017, 318(22): 2199-2210.
|
35 |
Bejnordi BE, Veta M, Van Diest PJ, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer [J]. JAMA, 2017, 318(22): 2199-2210.
|
36 |
Somashekhar SP, Sepulveda M, Puglielli S, et al. Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board [J]. Ann Oncol, 2018, 29(2): 418-423.
|
37 |
Loprinzi CL, Thome SD. Understanding the Utility of Adjuvant Systemic Therapy for Primary Breast Cancer [J]. J Clin Oncol, 2001, 19(4): 972-979
|
38 |
Zacharakis N, Chinnasamy H, Black MA, et al. Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer [J]. Nat Med, 2018, 24(6): 724-730.
|
39 |
Keren L, Bosse M, Marquez D, et al. A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging [J]. Cell, 2018, 174(6): 1373-1387.e19.
|
40 |
Lotsch J, Sipila R, Tasmuth T, et al. Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy [J]. Breast Cancer Res Treat, 2018, 171(2): 399-411.
|