192 related articles for article (PubMed ID: 36582788)
1. Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning.
Chen X; Zhang Y; Zhou J; Wang X; Liu X; Nie K; Lin X; He W; Su MY; Cao G; Wang M
Front Oncol; 2022; 12():991892. PubMed ID: 36582788
[TBL] [Abstract][Full Text] [Related]
2. Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer.
Wang G; Shi D; Guo Q; Zhang H; Wang S; Ren K
Front Oncol; 2022; 12():843436. PubMed ID: 35433437
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
Zhou J; Liu YL; Zhang Y; Chen JH; Combs FJ; Parajuli R; Mehta RS; Liu H; Chen Z; Zhao Y; Pan Z; Wang M; Yu R; Su MY
Front Oncol; 2021; 11():728224. PubMed ID: 34790569
[TBL] [Abstract][Full Text] [Related]
5. A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.
Jones MA; Sadeghipour N; Chen X; Islam W; Zheng B
Med Phys; 2023 Dec; 50(12):7670-7683. PubMed ID: 37083190
[TBL] [Abstract][Full Text] [Related]
6. Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features.
Zhang J; Zhan C; Zhang C; Song Y; Yan X; Guo Y; Ai T; Yang G
Radiol Med; 2023 Feb; 128(2):160-170. PubMed ID: 36670236
[TBL] [Abstract][Full Text] [Related]
7. Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.
Mendel K; Li H; Sheth D; Giger M
Acad Radiol; 2019 Jun; 26(6):735-743. PubMed ID: 30076083
[TBL] [Abstract][Full Text] [Related]
8. Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.
Zhou J; Zhang Y; Chang KT; Lee KE; Wang O; Li J; Lin Y; Pan Z; Chang P; Chow D; Wang M; Su MY
J Magn Reson Imaging; 2020 Mar; 51(3):798-809. PubMed ID: 31675151
[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. Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment.
Hao W; Gong J; Wang S; Zhu H; Zhao B; Peng W
Front Oncol; 2020; 10():531476. PubMed ID: 33194589
[TBL] [Abstract][Full Text] [Related]
11. An MRI-Based Radiomics Model for Predicting the Benignity and Malignancy of BI-RADS 4 Breast Lesions.
Zhang R; Wei W; Li R; Li J; Zhou Z; Ma M; Zhao R; Zhao X
Front Oncol; 2021; 11():733260. PubMed ID: 35155178
[TBL] [Abstract][Full Text] [Related]
12. Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance.
Debbi K; Habert P; Grob A; Loundou A; Siles P; Bartoli A; Jacquier A
Insights Imaging; 2023 Apr; 14(1):64. PubMed ID: 37052738
[TBL] [Abstract][Full Text] [Related]
13. Automated mammographic breast density estimation using a fully convolutional network.
Lee J; Nishikawa RM
Med Phys; 2018 Mar; 45(3):1178-1190. PubMed ID: 29363774
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy.
Favati B; Borgheresi R; Giannelli M; Marini C; Vani V; Marfisi D; Linsalata S; Moretti M; Mazzotta D; Neri E
Diagnostics (Basel); 2022 Mar; 12(4):. PubMed ID: 35453819
[TBL] [Abstract][Full Text] [Related]
16. Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters.
Zhou C; Xie H; Zhu F; Yan W; Yu R; Wang Y
Clin Exp Med; 2023 Oct; 23(6):2357-2368. PubMed ID: 36413273
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Pattern classification for breast lesion on FFDM by integration of radiomics and deep features.
Zhang X; Liang C; Zeng D; Jiang X; Zhong R; Lan Y; Ma J; Bai L
Comput Med Imaging Graph; 2021 Jun; 90():101922. PubMed ID: 34049119
[TBL] [Abstract][Full Text] [Related]
19. Diagnostic Performance of Digital Breast Tomosynthesis for Breast Suspicious Calcifications From Various Populations: A Comparison With Full-field Digital Mammography.
Li J; Zhang H; Jiang H; Guo X; Zhang Y; Qi D; Guan J; Liu Z; Wu E; Luo S
Comput Struct Biotechnol J; 2019; 17():82-89. PubMed ID: 30622686
[TBL] [Abstract][Full Text] [Related]
20. The Utility of the Fifth Edition of the BI-RADS Ultrasound Lexicon in Category 4 Breast Lesions: A Prospective Multicenter Study in China.
Gu Y; Tian JW; Ran HT; Ren WD; Chang C; Yuan JJ; Kang CS; Deng YB; Wang H; Luo BM; Guo SL; Zhou Q; Xue ES; Zhan WW; Zhou Q; Li J; Zhou P; Zhang CQ; Chen M; Gu Y; Xu JF; Chen W; Zhang YH; Wang HQ; Li JC; Wang HY; Jiang YX
Acad Radiol; 2022 Jan; 29 Suppl 1():S26-S34. PubMed ID: 32768352
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
