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For: Dromain C, Boyer B, Ferré R, Canale S, Delaloge S, Balleyguier C. Computed-aided diagnosis (CAD) in the detection of breast cancer. European Journal of Radiology 2013;82:417-23. [DOI: 10.1016/j.ejrad.2012.03.005] [Cited by in Crossref: 79] [Cited by in F6Publishing: 53] [Article Influence: 8.8] [Reference Citation Analysis]
Number Citing Articles
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3 Schulz-Wendtland R, Jud SM, Fasching PA, Hartmann A, Radicke M, Rauh C, Uder M, Wunderle M, Gass P, Langemann H, Beckmann MW, Emons J. A Standard Mammography Unit - Standard 3D Ultrasound Probe Fusion Prototype: First Results. Geburtshilfe Frauenheilkd 2017;77:679-85. [PMID: 28713173 DOI: 10.1055/s-0043-107034] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 1.2] [Reference Citation Analysis]
4 Miranda GH, Felipe JC. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput Biol Med 2015;64:334-46. [PMID: 25453323 DOI: 10.1016/j.compbiomed.2014.10.006] [Cited by in Crossref: 49] [Cited by in F6Publishing: 17] [Article Influence: 6.1] [Reference Citation Analysis]
5 Wolf M, Krause J, Carney PA, Bogart A, Kurvers RH. Collective intelligence meets medical decision-making: the collective outperforms the best radiologist. PLoS One 2015;10:e0134269. [PMID: 26267331 DOI: 10.1371/journal.pone.0134269] [Cited by in Crossref: 68] [Cited by in F6Publishing: 41] [Article Influence: 9.7] [Reference Citation Analysis]
6 Wang H, Feng J, Zhang Z, Su H, Cui L, He H, Liu L. Breast mass classification via deeply integrating the contextual information from multi-view data. Pattern Recognition 2018;80:42-52. [DOI: 10.1016/j.patcog.2018.02.026] [Cited by in Crossref: 34] [Cited by in F6Publishing: 8] [Article Influence: 8.5] [Reference Citation Analysis]
7 Jiang Y, Yang G, Liang Y, Shi Q, Cui B, Chang X, Qiu Z, Zhao X. Computer-Aided System Application Value for Assessing Hip Development. Front Physiol 2020;11:587161. [PMID: 33335486 DOI: 10.3389/fphys.2020.587161] [Reference Citation Analysis]
8 Altunkeser A, Körez MK. Usefulness of grayscale inverted images in addition to standard images in digital mammography. BMC Med Imaging 2017;17:26. [PMID: 28420325 DOI: 10.1186/s12880-017-0196-6] [Cited by in Crossref: 2] [Article Influence: 0.4] [Reference Citation Analysis]
9 Gómez-flores W, Hernández-lópez J. Assessment of the invariance and discriminant power of morphological features under geometric transformations for breast tumor classification. Computer Methods and Programs in Biomedicine 2020;185:105173. [DOI: 10.1016/j.cmpb.2019.105173] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 2.5] [Reference Citation Analysis]
10 Zeiser FA, da Costa CA, Zonta T, Marques NMC, Roehe AV, Moreno M, da Rosa Righi R. Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning. J Digit Imaging 2020;33:858-68. [PMID: 32206943 DOI: 10.1007/s10278-020-00330-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 6.0] [Reference Citation Analysis]
11 Choi JH, Kang BJ, Baek JE, Lee HS, Kim SH. Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography 2018;37:217-25. [PMID: 28992680 DOI: 10.14366/usg.17046] [Cited by in Crossref: 31] [Cited by in F6Publishing: 22] [Article Influence: 6.2] [Reference Citation Analysis]
12 Gopinath SCB, Perumal V, Xuan S. MicroRNA-155 complementation on a chemically functionalized dual electrode surface for determining breast cancer. 3 Biotech 2020;10:270. [PMID: 32523864 DOI: 10.1007/s13205-020-02261-x] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
13 Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology 2014;273:365-72. [PMID: 25028781 DOI: 10.1148/radiol.14132641] [Cited by in Crossref: 143] [Cited by in F6Publishing: 134] [Article Influence: 17.9] [Reference Citation Analysis]
14 Guo Y, Zhao W, Li S, Zhang Y, Lu Y. Automatic segmentation of the pectoral muscle based on boundary identification and shape prediction. Phys Med Biol 2020;65:045016. [PMID: 31869824 DOI: 10.1088/1361-6560/ab652b] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
15 Kim K, Song MK, Kim EK, Yoon JH. Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography. 2017;36:3-9. [PMID: 27184656 DOI: 10.14366/usg.16012] [Cited by in Crossref: 32] [Cited by in F6Publishing: 22] [Article Influence: 5.3] [Reference Citation Analysis]
16 Mann RM, Mus RD, van Zelst J, Geppert C, Karssemeijer N, Platel B. A Novel Approach to Contrast-Enhanced Breast Magnetic Resonance Imaging for Screening: High-Resolution Ultrafast Dynamic Imaging. Investigative Radiology 2014;49:579-85. [DOI: 10.1097/rli.0000000000000057] [Cited by in Crossref: 110] [Cited by in F6Publishing: 34] [Article Influence: 13.8] [Reference Citation Analysis]
17 Kendall EJ, Flynn MT. Automated breast image classification using features from its discrete cosine transform. PLoS One 2014;9:e91015. [PMID: 24632807 DOI: 10.1371/journal.pone.0091015] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
18 Celaya-padilla JM, Guzmán-valdivia CH, Galván-tejada CE, Galván-tejada JI, Gamboa-rosales H, Garza-veloz I, Martinez-fierro ML, Cid-báez MA, Martinez-torteya A, Martinez-ruiz FJ, Luna-garcía H, Moreno-baez A, Nandal A. Contralateral asymmetry for breast cancer detection: A CADx approach. Biocybernetics and Biomedical Engineering 2018;38:115-25. [DOI: 10.1016/j.bbe.2017.10.005] [Cited by in Crossref: 9] [Article Influence: 2.3] [Reference Citation Analysis]
19 Dhungel N, Carneiro G, Bradley AP. A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal 2017;37:114-28. [PMID: 28171807 DOI: 10.1016/j.media.2017.01.009] [Cited by in Crossref: 135] [Cited by in F6Publishing: 73] [Article Influence: 27.0] [Reference Citation Analysis]
20 Kim JJ, Kim JY, Kang HJ, Shin JK, Kang T, Lee SW, Bae YT. Computer-aided Diagnosis-generated Kinetic Features of Breast Cancer at Preoperative MR Imaging: Association with Disease-free Survival of Patients with Primary Operable Invasive Breast Cancer. Radiology 2017;284:45-54. [PMID: 28253106 DOI: 10.1148/radiol.2017162079] [Cited by in Crossref: 25] [Cited by in F6Publishing: 21] [Article Influence: 5.0] [Reference Citation Analysis]
21 Zhuang Z, Yang Z, Raj ANJ, Wei C, Jin P, Zhuang S. Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion. Comput Methods Programs Biomed 2021;208:106221. [PMID: 34144251 DOI: 10.1016/j.cmpb.2021.106221] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Sutton EJ, Dashevsky BZ, Oh JH, Veeraraghavan H, Apte AP, Thakur SB, Morris EA, Deasy JO. Breast cancer molecular subtype classifier that incorporates MRI features. J Magn Reson Imaging. 2016;44:122-129. [PMID: 26756416 DOI: 10.1002/jmri.25119] [Cited by in Crossref: 74] [Cited by in F6Publishing: 69] [Article Influence: 12.3] [Reference Citation Analysis]
23 Nam SY, Ko ES, Lim Y, Han BK, Ko EY, Choi JS, Lee JE. Preoperative dynamic breast magnetic resonance imaging kinetic features using computer-aided diagnosis: Association with survival outcome and tumor aggressiveness in patients with invasive breast cancer. PLoS One 2018;13:e0195756. [PMID: 29649266 DOI: 10.1371/journal.pone.0195756] [Cited by in Crossref: 11] [Cited by in F6Publishing: 6] [Article Influence: 2.8] [Reference Citation Analysis]
24 Schulz-Wendtland R, Wittenberg T, Michel T, Hartmann A, Beckmann MW, Rauh C, Jud SM, Brehm B, Meier-Meitinger M, Anton G, Uder M, Fasching PA. [Future of mammography-based imaging]. Radiologe 2014;54:217-23. [PMID: 24570108 DOI: 10.1007/s00117-013-2578-2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 0.6] [Reference Citation Analysis]
25 Wang J, Kato F, Yamashita H, Baba M, Cui Y, Li R, Oyama-Manabe N, Shirato H. Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer. J Digit Imaging 2017;30:215-27. [PMID: 27832519 DOI: 10.1007/s10278-016-9922-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
26 Di Segni M, de Soccio V, Cantisani V, Bonito G, Rubini A, Di Segni G, Lamorte S, Magri V, De Vito C, Migliara G, Bartolotta TV, Metere A, Giacomelli L, de Felice C, D'Ambrosio F. Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound. 2018;21:105-118. [PMID: 29681007 DOI: 10.1007/s40477-018-0297-2] [Cited by in Crossref: 22] [Cited by in F6Publishing: 21] [Article Influence: 5.5] [Reference Citation Analysis]
27 Xiao M, Zhao C, Zhu Q, Zhang J, Liu H, Li J, Jiang Y. An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions. J Thorac Dis 2019;11:5023-31. [PMID: 32030218 DOI: 10.21037/jtd.2019.12.10] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
28 Hameed Z, Zahia S, Garcia-Zapirain B, Javier Aguirre J, María Vanegas A. Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models. Sensors (Basel) 2020;20:E4373. [PMID: 32764398 DOI: 10.3390/s20164373] [Cited by in Crossref: 14] [Cited by in F6Publishing: 9] [Article Influence: 7.0] [Reference Citation Analysis]
29 Zhao C, Xiao M, Liu H, Wang M, Wang H, Zhang J, Jiang Y, Zhu Q. Reducing the number of unnecessary biopsies of US-BI-RADS 4a lesions through a deep learning method for residents-in-training: a cross-sectional study. BMJ Open 2020;10:e035757. [PMID: 32513885 DOI: 10.1136/bmjopen-2019-035757] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
30 Ferré R, Aldis A, Alsharif S, Omeroglu A, Mesurolle B. Differentiation of Fibroadenomas and Pure Mucinous Carcinomas on Dynamic Contrast-Enhanced MRI of the Breast Using Volume Segmentation for Kinetic Analysis: A Feasibility Study. American Journal of Roentgenology 2016;206:253-8. [DOI: 10.2214/ajr.15.14709] [Cited by in Crossref: 5] [Article Influence: 0.8] [Reference Citation Analysis]
31 Song Y, Zhang YD, Yan X, Liu H, Zhou M, Hu B, Yang G. Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI. J Magn Reson Imaging. 2018;48:1570-1577. [PMID: 29659067 DOI: 10.1002/jmri.26047] [Cited by in Crossref: 67] [Cited by in F6Publishing: 52] [Article Influence: 16.8] [Reference Citation Analysis]
32 Majumder MAA, Gaur U, Singh K, Kandamaran L, Gupta S, Haque M, Rahman S, Sa B, Rahman M, Rampersad F. Impact of COVID-19 pandemic on radiology education, training, and practice: A narrative review. World J Radiol 2021; 13(11): 354-370 [PMID: 34904050 DOI: 10.4329/wjr.v13.i11.354] [Reference Citation Analysis]
33 Xu X, Bao L, Tan Y, Zhu L, Kong F, Wang W. 1000-Case Reader Study of Radiologists' Performance in Interpretation of Automated Breast Volume Scanner Images with a Computer-Aided Detection System. Ultrasound Med Biol 2018;44:1694-702. [PMID: 29853222 DOI: 10.1016/j.ultrasmedbio.2018.04.020] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 1.8] [Reference Citation Analysis]
34 Jalalian A, Mashohor S, Mahmud R, Karasfi B, Saripan MIB, Ramli ARB. Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI J 2017;16:113-37. [PMID: 28435432 DOI: 10.17179/excli2016-701] [Cited by in F6Publishing: 7] [Reference Citation Analysis]
35 Shi P, Zhong J, Rampun A, Wang H. A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms. Computers in Biology and Medicine 2018;96:178-88. [DOI: 10.1016/j.compbiomed.2018.03.011] [Cited by in Crossref: 36] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
36 Mendelson DS, Rubin DL. Imaging informatics: essential tools for the delivery of imaging services. Acad Radiol 2013;20:1195-212. [PMID: 24029051 DOI: 10.1016/j.acra.2013.07.006] [Cited by in Crossref: 18] [Cited by in F6Publishing: 14] [Article Influence: 2.3] [Reference Citation Analysis]
37 Jian W, Sun X, Luo S. Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform. Biomed Eng Online 2012;11:96. [PMID: 23253202 DOI: 10.1186/1475-925X-11-96] [Cited by in Crossref: 20] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
38 Celaya-Padilla J, Martinez-Torteya A, Rodriguez-Rojas J, Galvan-Tejada J, Treviño V, Tamez-Peña J. Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms. Biomed Res Int 2015;2015:231656. [PMID: 26240818 DOI: 10.1155/2015/231656] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
39 Marin Z, Batchelder KA, Toner BC, Guimond L, Gerasimova-chechkina E, Harrow AR, Arneodo A, Khalil A. Mammographic evidence of microenvironment changes in tumorous breasts. Med Phys 2017;44:1324-36. [DOI: 10.1002/mp.12120] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 3.0] [Reference Citation Analysis]
40 Zhang Z, Zhang X, Lin X, Dong L, Zhang S, Zhang X, Sun D, Yuan K. Ultrasonic Diagnosis of Breast Nodules Using Modified Faster R-CNN. Ultrason Imaging 2019;41:353-67. [PMID: 31615352 DOI: 10.1177/0161734619882683] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
41 Posso M, Puig T, Carles M, Rué M, Canelo-Aybar C, Bonfill X. Effectiveness and cost-effectiveness of double reading in digital mammography screening: A systematic review and meta-analysis. Eur J Radiol 2017;96:40-9. [PMID: 29103474 DOI: 10.1016/j.ejrad.2017.09.013] [Cited by in Crossref: 17] [Cited by in F6Publishing: 11] [Article Influence: 3.4] [Reference Citation Analysis]
42 Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, Maley CC, Hwang ES, Lo JY. Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features? Acad Radiol 2017;24:1139-47. [PMID: 28506510 DOI: 10.1016/j.acra.2017.03.013] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 2.4] [Reference Citation Analysis]
43 Kim MY, Kim SY, Kim YS, Kim ES, Chang JM. Added value of deep learning-based computer-aided diagnosis and shear wave elastography to b-mode ultrasound for evaluation of breast masses detected by screening ultrasound. Medicine (Baltimore) 2021;100:e26823. [PMID: 34397844 DOI: 10.1097/MD.0000000000026823] [Reference Citation Analysis]
44 Zhang P, Ma Z, Zhang Y, Chen X, Wang G. Improved Inception V3 method and its effect on radiologists' performance of tumor classification with automated breast ultrasound system. Gland Surg 2021;10:2232-45. [PMID: 34422594 DOI: 10.21037/gs-21-328] [Reference Citation Analysis]
45 Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Geis JR, Pandharipande PV, Brink JA, Dreyer KJ. Current Applications and Future Impact of Machine Learning in Radiology. Radiology. 2018;288:318-328. [PMID: 29944078 DOI: 10.1148/radiol.2018171820] [Cited by in Crossref: 236] [Cited by in F6Publishing: 192] [Article Influence: 59.0] [Reference Citation Analysis]
46 Tan M, Pu J, Zheng B. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme. Med Phys 2014;41:081906. [PMID: 25086537 DOI: 10.1118/1.4890080] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 1.3] [Reference Citation Analysis]
47 Zhao C, Xiao M, Jiang Y, Liu H, Wang M, Wang H, Sun Q, Zhu Q. Feasibility of computer-assisted diagnosis for breast ultrasound: the results of the diagnostic performance of S-detect from a single center in China. Cancer Manag Res 2019;11:921-30. [PMID: 30774422 DOI: 10.2147/CMAR.S190966] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
48 Hendrix N, Hauber B, Lee CI, Bansal A, Veenstra DL. Artificial intelligence in breast cancer screening: primary care provider preferences. J Am Med Inform Assoc 2021;28:1117-24. [PMID: 33367670 DOI: 10.1093/jamia/ocaa292] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
49 Henriksen EL, Carlsen JF, Vejborg IM, Nielsen MB, Lauridsen CA. The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiol 2019;60:13-8. [PMID: 29665706 DOI: 10.1177/0284185118770917] [Cited by in Crossref: 31] [Cited by in F6Publishing: 17] [Article Influence: 7.8] [Reference Citation Analysis]
50 Magna G, Casti P, Jayaraman SV, Salmeri M, Mencattini A, Martinelli E, Natale CD. Identification of mammography anomalies for breast cancer detection by an ensemble of classification models based on artificial immune system. Knowledge-Based Systems 2016;101:60-70. [DOI: 10.1016/j.knosys.2016.02.019] [Cited by in Crossref: 21] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
51 Al-antari MA, Al-masni MA, Choi M, Han S, Kim T. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. International Journal of Medical Informatics 2018;117:44-54. [DOI: 10.1016/j.ijmedinf.2018.06.003] [Cited by in Crossref: 107] [Cited by in F6Publishing: 43] [Article Influence: 26.8] [Reference Citation Analysis]
52 Zhuang Z, Yang Z, Zhuang S, Joseph Raj AN, Yuan Y, Nersisson R. Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine. Comput Intell Neurosci 2021;2021:9980326. [PMID: 34113378 DOI: 10.1155/2021/9980326] [Reference Citation Analysis]
53 Rodríguez-Cristerna A, Gómez-Flores W, de Albuquerque Pereira WC. A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes. Comput Methods Programs Biomed 2018;153:33-40. [PMID: 29157459 DOI: 10.1016/j.cmpb.2017.10.004] [Cited by in Crossref: 17] [Cited by in F6Publishing: 7] [Article Influence: 3.4] [Reference Citation Analysis]
54 Wei CH, Gwo CY, Huang PJ. Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms. Br J Radiol 2016;89:20150802. [PMID: 27043966 DOI: 10.1259/bjr.20150802] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
55 Ha T, Kang DK, Kim TH. Percentage volume of delayed kinetics in computer-aided diagnosis of MRI of the breast to reduce false-positive results and unnecessary biopsies. Clin Radiol 2020;75:962.e1-8. [PMID: 32888654 DOI: 10.1016/j.crad.2020.08.005] [Reference Citation Analysis]
56 Choi WJ, Kim HH, Cha JH, Shin HJ, Chae EY. Comparison of Pathologic Response Evaluation Systems After Neoadjuvant Chemotherapy in Breast Cancers: Correlation With Computer-Aided Diagnosis of MRI Features. AJR Am J Roentgenol 2019;213:944-52. [PMID: 31237439 DOI: 10.2214/AJR.18.21016] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 2.3] [Reference Citation Analysis]
57 Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, Maley CC, Hwang ES, Lo JY. Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features. J Am Coll Radiol 2018;15:527-34. [PMID: 29398498 DOI: 10.1016/j.jacr.2017.11.036] [Cited by in Crossref: 37] [Cited by in F6Publishing: 25] [Article Influence: 9.3] [Reference Citation Analysis]