479 related articles for article (PubMed ID: 31385114)
1. 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]
2. 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]
3. 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]
4. Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learning classification performance on breast MRI.
Whitney HM; Drukker K; Vieceli M; Van Dusen A; de Oliveira M; Abe H; Giger ML
Med Phys; 2024 Mar; 51(3):1812-1821. PubMed ID: 37602841
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. A combined ultrasonic B-mode and color Doppler system for the classification of breast masses using neural network.
Qian X; Zhang B; Liu S; Wang Y; Chen X; Liu J; Yang Y; Chen X; Wei Y; Xiao Q; Ma J; Shung KK; Zhou Q; Liu L; Chen Z
Eur Radiol; 2020 May; 30(5):3023-3033. PubMed ID: 32006174
[TBL] [Abstract][Full Text] [Related]
7. Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.
Ciritsis A; Rossi C; Eberhard M; Marcon M; Becker AS; Boss A
Eur Radiol; 2019 Oct; 29(10):5458-5468. PubMed ID: 30927100
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. 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]
11. Added Value of Quantitative Ultrasound and Machine Learning in BI-RADS 4-5 Assessment of Solid Breast Lesions.
Destrempes F; Trop I; Allard L; Chayer B; Garcia-Duitama J; El Khoury M; Lalonde L; Cloutier G
Ultrasound Med Biol; 2020 Feb; 46(2):436-444. PubMed ID: 31785840
[TBL] [Abstract][Full Text] [Related]
12. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution.
Ji Y; Li H; Edwards AV; Papaioannou J; Ma W; Liu P; Giger ML
Cancer Imaging; 2019 Sep; 19(1):64. PubMed ID: 31533838
[TBL] [Abstract][Full Text] [Related]
13. A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features.
Sakai A; Onishi Y; Matsui M; Adachi H; Teramoto A; Saito K; Fujita H
Radiol Phys Technol; 2020 Mar; 13(1):27-36. PubMed ID: 31686300
[TBL] [Abstract][Full Text] [Related]
14. Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.
Tahmassebi A; Wengert GJ; Helbich TH; Bago-Horvath Z; Alaei S; Bartsch R; Dubsky P; Baltzer P; Clauser P; Kapetas P; Morris EA; Meyer-Baese A; Pinker K
Invest Radiol; 2019 Feb; 54(2):110-117. PubMed ID: 30358693
[TBL] [Abstract][Full Text] [Related]
15. Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts.
Hamyoon H; Yee Chan W; Mohammadi A; Yusuf Kuzan T; Mirza-Aghazadeh-Attari M; Leong WL; Murzoglu Altintoprak K; Vijayananthan A; Rahmat K; Ab Mumin N; Sam Leong S; Ejtehadifar S; Faeghi F; Abolghasemi J; Ciaccio EJ; Rajendra Acharya U; Abbasian Ardakani A
Eur J Radiol; 2022 Dec; 157():110591. PubMed ID: 36356463
[TBL] [Abstract][Full Text] [Related]
16. 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.
Zhao C; Xiao M; Liu H; Wang M; Wang H; Zhang J; Jiang Y; Zhu Q
BMJ Open; 2020 Jun; 10(6):e035757. PubMed ID: 32513885
[TBL] [Abstract][Full Text] [Related]
17. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics.
Mao B; Ma J; Duan S; Xia Y; Tao Y; Zhang L
Eur Radiol; 2021 Jul; 31(7):4576-4586. PubMed ID: 33447862
[TBL] [Abstract][Full Text] [Related]
18. Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study.
Marcon M; Ciritsis A; Rossi C; Becker AS; Berger N; Wurnig MC; Wagner MW; Frauenfelder T; Boss A
Eur Radiol Exp; 2019 Nov; 3(1):44. PubMed ID: 31676937
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability.
Kim SM; Han H; Park JM; Choi YJ; Yoon HS; Sohn JH; Baek MH; Kim YN; Chae YM; June JJ; Lee J; Jeon YH
J Digit Imaging; 2012 Oct; 25(5):599-606. PubMed ID: 22270787
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]