199 related articles for article (PubMed ID: 35295204)
1. Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors.
Muhtadi S
Comput Math Methods Med; 2022; 2022():1633858. PubMed ID: 35295204
[TBL] [Abstract][Full Text] [Related]
2. A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images.
Wei M; Du Y; Wu X; Su Q; Zhu J; Zheng L; Lv G; Zhuang J
Comput Math Methods Med; 2020; 2020():5894010. PubMed ID: 33062038
[TBL] [Abstract][Full Text] [Related]
3. Robust phase-based texture descriptor for classification of breast ultrasound images.
Cai L; Wang X; Wang Y; Guo Y; Yu J; Wang Y
Biomed Eng Online; 2015 Mar; 14():26. PubMed ID: 25889570
[TBL] [Abstract][Full Text] [Related]
4. Quantification of elastic heterogeneity using contourlet-based texture analysis in shear-wave elastography for breast tumor classification.
Zhang Q; Xiao Y; Chen S; Wang C; Zheng H
Ultrasound Med Biol; 2015 Feb; 41(2):588-600. PubMed ID: 25444693
[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. 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]
7. Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images.
Moon WK; Shen YW; Huang CS; Chiang LR; Chang RF
Ultrasound Med Biol; 2011 Apr; 37(4):539-48. PubMed ID: 21420580
[TBL] [Abstract][Full Text] [Related]
8. Breast tumor classification using different features of quantitative ultrasound parametric images.
Hsu SM; Kuo WH; Kuo FC; Liao YY
Int J Comput Assist Radiol Surg; 2019 Apr; 14(4):623-633. PubMed ID: 30617720
[TBL] [Abstract][Full Text] [Related]
9. Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions.
Osapoetra LO; Chan W; Tran W; Kolios MC; Czarnota GJ
PLoS One; 2020; 15(12):e0244965. PubMed ID: 33382837
[TBL] [Abstract][Full Text] [Related]
10. Classification of breast tumors using sonographic texture analysis.
Ardakani AA; Gharbali A; Mohammadi A
J Ultrasound Med; 2015 Feb; 34(2):225-31. PubMed ID: 25614395
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Computer-aided diagnosis with textural features for breast lesions in sonograms.
Chen DR; Huang YL; Lin SH
Comput Med Imaging Graph; 2011 Apr; 35(3):220-6. PubMed ID: 21131178
[TBL] [Abstract][Full Text] [Related]
13. Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images.
Wu WJ; Lin SW; Moon WK
Comput Med Imaging Graph; 2012 Dec; 36(8):627-33. PubMed ID: 22939834
[TBL] [Abstract][Full Text] [Related]
14. Dual-modal computer-assisted evaluation of axillary lymph node metastasis in breast cancer patients on both real-time elastography and B-mode ultrasound.
Zhang Q; Suo J; Chang W; Shi J; Chen M
Eur J Radiol; 2017 Oct; 95():66-74. PubMed ID: 28987700
[TBL] [Abstract][Full Text] [Related]
15. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
Lee HS; Hong H; Jung DC; Park S; Kim J
Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
[TBL] [Abstract][Full Text] [Related]
16. Ultrasound Image Texture Feature Learning-Based Breast Cancer Benign and Malignant Classification.
Gong H; Qian M; Pan G; Hu B
Comput Math Methods Med; 2021; 2021():6261032. PubMed ID: 35391913
[TBL] [Abstract][Full Text] [Related]
17. [Multi-feature Extraction and Classification of Breast Tumor in Ultrasound Image].
Ren L; Liu Y; Tong Y; Cao X; Wu Y
Zhongguo Yi Liao Qi Xie Za Zhi; 2020 Apr; 44(4):294-301. PubMed ID: 32762200
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images.
Ma H; Tian R; Li H; Sun H; Lu G; Liu R; Wang Z
Biomed Eng Online; 2021 Nov; 20(1):112. PubMed ID: 34794443
[TBL] [Abstract][Full Text] [Related]
20. Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.
Milenković J; Hertl K; Košir A; Zibert J; Tasič JF
Artif Intell Med; 2013 Jun; 58(2):101-14. PubMed ID: 23548472
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
