255 related articles for article (PubMed ID: 28409843)
1. Reproducibility of quantitative high-throughput BI-RADS features extracted from ultrasound images of breast cancer.
Hu Y; Qiao M; Guo Y; Wang Y; Yu J; Li J; Chang C
Med Phys; 2017 Jul; 44(7):3676-3685. PubMed ID: 28409843
[TBL] [Abstract][Full Text] [Related]
2. Breast DCE-MRI radiomics: a robust computer-aided system based on reproducible BI-RADS features across the influence of datasets bias and segmentation methods.
Qiao M; Li C; Suo S; Cheng F; Hua J; Xue D; Guo Y; Xu J; Wang Y
Int J Comput Assist Radiol Surg; 2020 Jun; 15(6):921-930. PubMed ID: 32388693
[TBL] [Abstract][Full Text] [Related]
3. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.
Shan J; Alam SK; Garra B; Zhang Y; Ahmed T
Ultrasound Med Biol; 2016 Apr; 42(4):980-8. PubMed ID: 26806441
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images.
Fleury E; Marcomini K
Eur Radiol Exp; 2019 Aug; 3(1):34. PubMed ID: 31385114
[TBL] [Abstract][Full Text] [Related]
6. Breast Tumor Classification Based on a Computerized Breast Imaging Reporting and Data System Feature System.
Qiao M; Hu Y; Guo Y; Wang Y; Yu J
J Ultrasound Med; 2018 Feb; 37(2):403-415. PubMed ID: 28804937
[TBL] [Abstract][Full Text] [Related]
7. A set of shear wave elastography quantitative parameters combined with ultrasound BI-RADS to assess benign and malignant breast lesions.
Shi XQ; Li JL; Wan WB; Huang Y
Ultrasound Med Biol; 2015 Apr; 41(4):960-6. PubMed ID: 25701532
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. EMD-DWT based transform domain feature reduction approach for quantitative multi-class classification of breast lesions.
Ara SR; Bashar SK; Alam F; Hasan MK
Ultrasonics; 2017 Sep; 80():22-33. PubMed ID: 28499122
[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. Automated BI-RADS classification of lesions using pyramid triple deep feature generator technique on breast ultrasound images.
Kaplan E; Chan WY; Dogan S; Barua PD; Bulut HT; Tuncer T; Cizik M; Tan RS; Acharya UR
Med Eng Phys; 2022 Oct; 108():103895. PubMed ID: 36195364
[TBL] [Abstract][Full Text] [Related]
12. A case-oriented web-based training system for breast cancer diagnosis.
Huang Q; Huang X; Liu L; Lin Y; Long X; Li X
Comput Methods Programs Biomed; 2018 Mar; 156():73-83. PubMed ID: 29428078
[TBL] [Abstract][Full Text] [Related]
13. What Help Could Ultrasound Elastography Give to the Diagnosis of Breast Papillary Lesions?
Li LJ; Yao JY; Zhou XC; Zhao XB; Zhong WJ; Ou B; Luo BM; Hao SY; Zhi H
Ultrasound Med Biol; 2017 May; 43(5):903-910. PubMed ID: 28256344
[TBL] [Abstract][Full Text] [Related]
14. Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model.
Hu Y; Guo Y; Wang Y; Yu J; Li J; Zhou S; Chang C
Med Phys; 2019 Jan; 46(1):215-228. PubMed ID: 30374980
[TBL] [Abstract][Full Text] [Related]
15. The Diagnostic Value of 3D Power Doppler Ultrasound Combined With VOCAL in the Vascular Distribution of Breast Masses.
Wang H; Yan B; Yue L; He M; Liu Y; Li H
Acad Radiol; 2020 Feb; 27(2):198-203. PubMed ID: 31053481
[TBL] [Abstract][Full Text] [Related]
16. Comparison of Inter-Observer Variability and Diagnostic Performance of the Fifth Edition of BI-RADS for Breast Ultrasound of Static versus Video Images.
Youk JH; Jung I; Yoon JH; Kim SH; Kim YM; Lee EH; Jeong SH; Kim MJ
Ultrasound Med Biol; 2016 Sep; 42(9):2083-8. PubMed ID: 27324292
[TBL] [Abstract][Full Text] [Related]
17. Usefulness of combined BI-RADS analysis and Nakagami statistics of ultrasound echoes in the diagnosis of breast lesions.
Dobruch-Sobczak K; Piotrzkowska-Wróblewska H; Roszkowska-Purska K; Nowicki A; Jakubowski W
Clin Radiol; 2017 Apr; 72(4):339.e7-339.e15. PubMed ID: 28038779
[TBL] [Abstract][Full Text] [Related]
18. The uncertainty of boundary can improve the classification accuracy of BI-RADS 4A ultrasound image.
Wang H; Hu Y; Lu Y; Zhou J; Guo Y
Med Phys; 2022 May; 49(5):3314-3324. PubMed ID: 35261034
[TBL] [Abstract][Full Text] [Related]
19. Comparison of strain and acoustic radiation force impulse elastography of breast lesions by qualitative evaluation.
Zhao Q; Wang XL; Sun JW; Jiang ZP; Tao L; Zhou XL
Clin Hemorheol Microcirc; 2018; 70(1):39-50. PubMed ID: 29660916
[TBL] [Abstract][Full Text] [Related]
20. Multi-Task/Single-Task Joint Learning of Ultrasound BI-RADS Features.
Huang Q; Ye L
IEEE Trans Ultrason Ferroelectr Freq Control; 2022 Feb; 69(2):691-701. PubMed ID: 34871170
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]