251 related articles for article (PubMed ID: 33432076)
1. Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses.
Kim S-; Choi Y; Kim E-; Han BK; Yoon JH; Choi JS; Chang JM
Sci Rep; 2021 Jan; 11(1):395. PubMed ID: 33432076
[TBL] [Abstract][Full Text] [Related]
2. The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study.
Wei Q; Yan YJ; Wu GG; Ye XR; Jiang F; Liu J; Wang G; Wang Y; Song J; Pan ZP; Hu JH; Jin CY; Wang X; Dietrich CF; Cui XW
Eur Radiol; 2022 Jun; 32(6):4046-4055. PubMed ID: 35066633
[TBL] [Abstract][Full Text] [Related]
3. Computer-Aided Diagnosis in Multiparametric Magnetic Resonance Imaging Screening of Women With Extremely Dense Breasts to Reduce False-Positive Diagnoses.
Verburg E; van Gils CH; Bakker MF; Viergever MA; Pijnappel RM; Veldhuis WB; Gilhuijs KGA
Invest Radiol; 2020 Jul; 55(7):438-444. PubMed ID: 32149858
[TBL] [Abstract][Full Text] [Related]
4. Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography.
Choi JS; Han BK; Ko ES; Bae JM; Ko EY; Song SH; Kwon MR; Shin JH; Hahn SY
Korean J Radiol; 2019 May; 20(5):749-758. PubMed ID: 30993926
[TBL] [Abstract][Full Text] [Related]
5. Quantitative ultrasound analysis for classification of BI-RADS category 3 breast masses.
Moon WK; Lo CM; Chang JM; Huang CS; Chen JH; Chang RF
J Digit Imaging; 2013 Dec; 26(6):1091-8. PubMed ID: 23494603
[TBL] [Abstract][Full Text] [Related]
6. A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound: Added value for the inexperienced breast radiologist.
Park HJ; Kim SM; La Yun B; Jang M; Kim B; Jang JY; Lee JY; Lee SH
Medicine (Baltimore); 2019 Jan; 98(3):e14146. PubMed ID: 30653149
[TBL] [Abstract][Full Text] [Related]
7. Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise.
He P; Chen W; Bai MY; Li J; Wang QQ; Fan LH; Zheng J; Liu CT; Zhang XR; Yuan XR; Song PJ; Cui LG
AJR Am J Roentgenol; 2023 Oct; 221(4):450-459. PubMed ID: 37222275
[No Abstract] [Full Text] [Related]
8. Breast masses: computer-aided diagnosis with serial mammograms.
Hadjiiski L; Sahiner B; Helvie MA; Chan HP; Roubidoux MA; Paramagul C; Blane C; Petrick N; Bailey J; Klein K; Foster M; Patterson SK; Adler D; Nees AV; Shen J
Radiology; 2006 Aug; 240(2):343-56. PubMed ID: 16801362
[TBL] [Abstract][Full Text] [Related]
9. Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study.
Gu Y; Xu W; Liu T; An X; Tian J; Ran H; Ren W; Chang C; Yuan J; Kang C; Deng Y; Wang H; Luo B; Guo S; Zhou Q; Xue E; Zhan W; Zhou Q; Li J; Zhou P; Chen M; Gu Y; Chen W; Zhang Y; Li J; Cong L; Zhu L; Wang H; Jiang Y
Eur Radiol; 2023 Apr; 33(4):2954-2964. PubMed ID: 36418619
[TBL] [Abstract][Full Text] [Related]
10. Computer aided classification system for breast ultrasound based on Breast Imaging Reporting and Data System (BI-RADS).
Shen WC; Chang RF; Moon WK
Ultrasound Med Biol; 2007 Nov; 33(11):1688-98. PubMed ID: 17681678
[TBL] [Abstract][Full Text] [Related]
11. A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes.
Rodríguez-Cristerna A; Gómez-Flores W; de Albuquerque Pereira WC
Comput Methods Programs Biomed; 2018 Jan; 153():33-40. PubMed ID: 29157459
[TBL] [Abstract][Full Text] [Related]
12. Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection Software for Automated Breast Ultrasound.
Kwon MR; Youn I; Lee MY; Lee HA
Acad Radiol; 2024 Feb; 31(2):480-491. PubMed ID: 37813703
[TBL] [Abstract][Full Text] [Related]
13. Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study.
Chan HP; Sahiner B; Helvie MA; Petrick N; Roubidoux MA; Wilson TE; Adler DD; Paramagul C; Newman JS; Sanjay-Gopal S
Radiology; 1999 Sep; 212(3):817-27. PubMed ID: 10478252
[TBL] [Abstract][Full Text] [Related]
14. Computer-aided diagnosis based on quantitative elastographic features with supersonic shear wave imaging.
Xiao Y; Zeng J; Niu L; Zeng Q; Wu T; Wang C; Zheng R; Zheng H
Ultrasound Med Biol; 2014 Feb; 40(2):275-86. PubMed ID: 24268454
[TBL] [Abstract][Full Text] [Related]
15. Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial.
Verburg E; van Gils CH; van der Velden BHM; Bakker MF; Pijnappel RM; Veldhuis WB; Gilhuijs KGA
Invest Radiol; 2023 Apr; 58(4):293-298. PubMed ID: 36256783
[TBL] [Abstract][Full Text] [Related]
16. Computer-aided diagnosis of breast masses using quantified BI-RADS findings.
Moon WK; Lo CM; Cho N; Chang JM; Huang CS; Chen JH; Chang RF
Comput Methods Programs Biomed; 2013 Jul; 111(1):84-92. PubMed ID: 23639752
[TBL] [Abstract][Full Text] [Related]
17. Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts.
van Zelst JCM; Tan T; Clauser P; Domingo A; Dorrius MD; Drieling D; Golatta M; Gras F; de Jong M; Pijnappel R; Rutten MJCM; Karssemeijer N; Mann RM
Eur Radiol; 2018 Jul; 28(7):2996-3006. PubMed ID: 29417251
[TBL] [Abstract][Full Text] [Related]
18. Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.
Patel BK; Ranjbar S; Wu T; Pockaj BA; Li J; Zhang N; Lobbes M; Zhang B; Mitchell JR
Eur J Radiol; 2018 Jan; 98():207-213. PubMed ID: 29279165
[TBL] [Abstract][Full Text] [Related]
19. Evaluation of the effect of computer aided diagnosis system on breast ultrasound for inexperienced radiologists in describing and determining breast lesions.
Lee J; Kim S; Kang BJ; Kim SH; Park GE
Med Ultrason; 2019 Aug; 21(3):239-245. PubMed ID: 31476202
[TBL] [Abstract][Full Text] [Related]
20. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening.
Liu H; Chen Y; Zhang Y; Wang L; Luo R; Wu H; Wu C; Zhang H; Tan W; Yin H; Wang D
Eur Radiol; 2021 Aug; 31(8):5902-5912. PubMed ID: 33496829
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
