143 related articles for article (PubMed ID: 37937827)
1. BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems.
Gómez-Flores W; Gregorio-Calas MJ; Coelho de Albuquerque Pereira W
Med Phys; 2024 Apr; 51(4):3110-3123. PubMed ID: 37937827
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. Fully automatic tumor segmentation of breast ultrasound images with deep learning.
Zhang S; Liao M; Wang J; Zhu Y; Zhang Y; Zhang J; Zheng R; Lv L; Zhu D; Chen H; Wang W
J Appl Clin Med Phys; 2023 Jan; 24(1):e13863. PubMed ID: 36495018
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images.
Daoud MI; Al-Ali A; Alazrai R; Al-Najar MS; Alsaify BA; Ali MZ; Alouneh S
Sensors (Basel); 2022 Sep; 22(18):. PubMed ID: 36146070
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. BUS-Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets.
Thomas C; Byra M; Marti R; Yap MH; Zwiggelaar R
Med Phys; 2023 May; 50(5):3223-3243. PubMed ID: 36794706
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images.
Tasnim J; Hasan MK
Phys Med Biol; 2023 Dec; 69(1):. PubMed ID: 38056017
[No Abstract] [Full Text] [Related]
12. 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]
13. A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound.
Gómez-Flores W; Coelho de Albuquerque Pereira W
Comput Biol Med; 2020 Nov; 126():104036. PubMed ID: 33059238
[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. Breast ultrasound image segmentation: a survey.
Huang Q; Luo Y; Zhang Q
Int J Comput Assist Radiol Surg; 2017 Mar; 12(3):493-507. PubMed ID: 28070777
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Semi-supervised segmentation of lesion from breast ultrasound images with attentional generative adversarial network.
Han L; Huang Y; Dou H; Wang S; Ahamad S; Luo H; Liu Q; Fan J; Zhang J
Comput Methods Programs Biomed; 2020 Jun; 189():105275. PubMed ID: 31978805
[TBL] [Abstract][Full Text] [Related]
18. Segmentation-based BI-RADS ensemble classification of breast tumours in ultrasound images.
Bobowicz M; Badocha M; Gwozdziewicz K; Rygusik M; Kalinowska P; Szurowska E; Dziubich T
Int J Med Inform; 2024 Jun; 189():105522. PubMed ID: 38852288
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network.
Hejduk P; Marcon M; Unkelbach J; Ciritsis A; Rossi C; Borkowski K; Boss A
Eur Radiol; 2022 Jul; 32(7):4868-4878. PubMed ID: 35147776
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
