167 related articles for article (PubMed ID: 37284087)
1. Development and validation of a transformer-based CAD model for improving the consistency of BI-RADS category 3-5 nodule classification among radiologists: a multiple center study.
Ji H; Zhu Q; Ma T; Cheng Y; Zhou S; Ren W; Huang H; He W; Ran H; Ruan L; Guo Y; Tian J; Chen W; Chen L; Wang Z; Zhou Q; Niu L; Zhang W; Yang R; Chen Q; Zhang R; Wang H; Li L; Liu M; Nie F; Zhou A
Quant Imaging Med Surg; 2023 Jun; 13(6):3671-3687. PubMed ID: 37284087
[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. 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]
4. Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists' Diagnosis Performance? An Observer Study.
He Z; Li Y; Zeng W; Xu W; Liu J; Ma X; Wei J; Zeng H; Xu Z; Wang S; Wen C; Wu J; Feng C; Ma M; Qin G; Lu Y; Chen W
Front Oncol; 2021; 11():773389. PubMed ID: 34976817
[TBL] [Abstract][Full Text] [Related]
5. The use of modified TI-RADS using contrast-enhanced ultrasound features for classification purposes in the differential diagnosis of benign and malignant thyroid nodules: A prospective and multi-center study.
Zhou P; Chen F; Zhou P; Xu L; Wang L; Wang Z; Yu Y; Liu X; Wang B; Yan W; Zhou H; Tao Y; Liu W
Front Endocrinol (Lausanne); 2023; 14():1080908. PubMed ID: 36817602
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Differential Diagnosis of Breast Category 3 and 4 Nodules Through BI-RADS Classification in Conjunction with Shear Wave Elastography.
Wang M; Yang Z; Liu C; Yan J; Zhang W; Sun J; Cui G
Ultrasound Med Biol; 2017 Mar; 43(3):601-606. PubMed ID: 27988221
[TBL] [Abstract][Full Text] [Related]
8. Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience.
Jin Z; Zhu Y; Zhang S; Xie F; Zhang M; Zhang Y; Tian X; Zhang J; Luo Y; Cao J
Med Sci Monit; 2020 Jan; 26():e918452. PubMed ID: 31929498
[TBL] [Abstract][Full Text] [Related]
9. Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis.
Cui Q; Sun L; Zhang Y; Zhao Z; Li S; Liu Y; Ge H; Qin D; Zhao Y
Ann Transl Med; 2021 Nov; 9(22):1677. PubMed ID: 34988186
[TBL] [Abstract][Full Text] [Related]
10. Computer-aided classification of BI-RADS category 3 breast lesions.
Buchbinder SS; Leichter IS; Lederman RB; Novak B; Bamberger PN; Sklair-Levy M; Yarmish G; Fields SI
Radiology; 2004 Mar; 230(3):820-3. PubMed ID: 14739315
[TBL] [Abstract][Full Text] [Related]
11. [Differential diagnosis model of benign and malignant breast BI-RADS category 4 nodules based on serum SP70 and conventional laboratory indicators].
Ding HM; Xu J; Wang F; Zhang Q; Pan H; Mu Y; Gu CR; Miao SX; Li XN; Ju HY; Wang L; Pan SY
Zhonghua Yu Fang Yi Xue Za Zhi; 2022 Dec; 56(12):1774-1783. PubMed ID: 36536565
[No Abstract] [Full Text] [Related]
12. 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]
13. Predicting Breast Cancer in Breast Imaging Reporting and Data System (BI-RADS) Ultrasound Category 4 or 5 Lesions: A Nomogram Combining Radiomics and BI-RADS.
Luo WQ; Huang QX; Huang XW; Hu HT; Zeng FQ; Wang W
Sci Rep; 2019 Aug; 9(1):11921. PubMed ID: 31417138
[TBL] [Abstract][Full Text] [Related]
14. Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks.
Xu P; Zhao J; Wan M; Song Q; Su Q; Wang D
Med Phys; 2024 Jun; 51(6):4243-4257. PubMed ID: 38436433
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. The diagnostic value of MRI for architectural distortion categorized as BI-RADS category 3-4 by mammography.
Mei H; Xu J; Yao G; Wang Y
Gland Surg; 2020 Aug; 9(4):1008-1018. PubMed ID: 32953609
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. A simplified scoring protocol to improve diagnostic accuracy with the breast imaging reporting and data system in breast magnetic resonance imaging.
Zhong Y; Li M; Zhu J; Zhang B; Liu M; Wang Z; Wang J; Zheng Y; Cheng L; Li X
Quant Imaging Med Surg; 2022 Jul; 12(7):3860-3872. PubMed ID: 35782247
[TBL] [Abstract][Full Text] [Related]
19. A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses.
Interlenghi M; Salvatore C; Magni V; Caldara G; Schiavon E; Cozzi A; Schiaffino S; Carbonaro LA; Castiglioni I; Sardanelli F
Diagnostics (Basel); 2022 Jan; 12(1):. PubMed ID: 35054354
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
