297 related articles for article (PubMed ID: 35574397)
1. Generating Full-Field Digital Mammogram From Digitized Screen-Film Mammogram for Breast Cancer Screening With High-Resolution Generative Adversarial Network.
Zhou Y; Wei J; Wu D; Zhang Y
Front Oncol; 2022; 12():868257. PubMed ID: 35574397
[TBL] [Abstract][Full Text] [Related]
2. Multi-scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model-based perceptual similarity.
Jiang G; He Z; Zhou Y; Wei J; Xu Y; Zeng H; Wu J; Qin G; Chen W; Lu Y
Med Phys; 2023 Feb; 50(2):837-853. PubMed ID: 36196045
[TBL] [Abstract][Full Text] [Related]
3. SAP-cGAN: Adversarial learning for breast mass segmentation in digital mammogram based on superpixel average pooling.
Li Y; Zhao G; Zhang Q; Lin Y; Wang M
Med Phys; 2021 Mar; 48(3):1157-1167. PubMed ID: 33340125
[TBL] [Abstract][Full Text] [Related]
4. Deep Learning to Improve Breast Cancer Detection on Screening Mammography.
Shen L; Margolies LR; Rothstein JH; Fluder E; McBride R; Sieh W
Sci Rep; 2019 Aug; 9(1):12495. PubMed ID: 31467326
[TBL] [Abstract][Full Text] [Related]
5. Full-field digital mammographic interpretation with prior analog versus prior digitized analog mammography: time for interpretation.
Garg AS; Rapelyea JA; Rechtman LR; Torrente J; Bittner RB; Coffey CM; Brem RF
AJR Am J Roentgenol; 2011 Jun; 196(6):1436-8. PubMed ID: 21606310
[TBL] [Abstract][Full Text] [Related]
6. Automatic mass detection in mammograms using deep convolutional neural networks.
Agarwal R; Diaz O; Lladó X; Yap MH; Martí R
J Med Imaging (Bellingham); 2019 Jul; 6(3):031409. PubMed ID: 35834317
[TBL] [Abstract][Full Text] [Related]
7. Computer-aided detection systems for breast masses: comparison of performances on full-field digital mammograms and digitized screen-film mammograms.
Wei J; Hadjiiski LM; Sahiner B; Chan HP; Ge J; Roubidoux MA; Helvie MA; Zhou C; Wu YT; Paramagul C; Zhang Y
Acad Radiol; 2007 Jun; 14(6):659-69. PubMed ID: 17502255
[TBL] [Abstract][Full Text] [Related]
8. Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography.
Hwang I; Trivedi H; Brown-Mulry B; Zhang L; Nalla V; Gastounioti A; Gichoya J; Seyyed-Kalantari L; Banerjee I; Woo M
Front Radiol; 2023; 3():1181190. PubMed ID: 37588666
[TBL] [Abstract][Full Text] [Related]
9. Deep learning for mass detection in Full Field Digital Mammograms.
Agarwal R; Díaz O; Yap MH; Lladó X; Martí R
Comput Biol Med; 2020 Jun; 121():103774. PubMed ID: 32339095
[TBL] [Abstract][Full Text] [Related]
10. FSE-Net: feature selection and enhancement network for mammogram classification.
Liao C; Wen X; Qi S; Liu Y; Cao R
Phys Med Biol; 2023 Sep; 68(19):. PubMed ID: 37712226
[No Abstract] [Full Text] [Related]
11. Learning from adversarial medical images for X-ray breast mass segmentation.
Shen T; Gou C; Wang FY; He Z; Chen W
Comput Methods Programs Biomed; 2019 Oct; 180():105012. PubMed ID: 31421601
[TBL] [Abstract][Full Text] [Related]
12. YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms.
Su Y; Liu Q; Xie W; Hu P
Comput Methods Programs Biomed; 2022 Jun; 221():106903. PubMed ID: 35636358
[TBL] [Abstract][Full Text] [Related]
13. Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening.
Aboutalib SS; Mohamed AA; Berg WA; Zuley ML; Sumkin JH; Wu S
Clin Cancer Res; 2018 Dec; 24(23):5902-5909. PubMed ID: 30309858
[TBL] [Abstract][Full Text] [Related]
14. Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset.
Sawyer Lee R; Dunnmon JA; He A; Tang S; Ré C; Rubin DL
J Biomed Inform; 2021 Jan; 113():103656. PubMed ID: 33309994
[TBL] [Abstract][Full Text] [Related]
15. Synthesis of Mammogram From Digital Breast Tomosynthesis Using Deep Convolutional Neural Network With Gradient Guided cGANs.
Jiang G; Wei J; Xu Y; He Z; Zeng H; Wu J; Qin G; Chen W; Lu Y
IEEE Trans Med Imaging; 2021 Aug; 40(8):2080-2091. PubMed ID: 33826513
[TBL] [Abstract][Full Text] [Related]
16. Connected-UNets: a deep learning architecture for breast mass segmentation.
Baccouche A; Garcia-Zapirain B; Castillo Olea C; Elmaghraby AS
NPJ Breast Cancer; 2021 Dec; 7(1):151. PubMed ID: 34857755
[TBL] [Abstract][Full Text] [Related]
17. HRGAN: A Generative Adversarial Network Producing Higher-Resolution Images than Training Sets.
Park M; Lee M; Yu S
Sensors (Basel); 2022 Feb; 22(4):. PubMed ID: 35214337
[TBL] [Abstract][Full Text] [Related]
18. Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm.
Jiang J; Peng J; Hu C; Jian W; Wang X; Liu W
Artif Intell Med; 2022 Dec; 134():102419. PubMed ID: 36462904
[TBL] [Abstract][Full Text] [Related]
19. DV-DCNN: Dual-view deep convolutional neural network for matching detected masses in mammograms.
AlGhamdi M; Abdel-Mottaleb M
Comput Methods Programs Biomed; 2021 Aug; 207():106152. PubMed ID: 34058629
[TBL] [Abstract][Full Text] [Related]
20. MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms.
Nissar I; Alam S; Masood S; Kashif M
Comput Methods Programs Biomed; 2024 May; 248():108121. PubMed ID: 38531147
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
