145 related articles for article (PubMed ID: 36011022)
1. Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images.
Alkhaleefah M; Tan TH; Chang CH; Wang TC; Ma SC; Chang L; Chang YL
Cancers (Basel); 2022 Aug; 14(16):. PubMed ID: 36011022
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Convolutional neural network for automated mass segmentation in mammography.
Abdelhafiz D; Bi J; Ammar R; Yang C; Nabavi S
BMC Bioinformatics; 2020 Dec; 21(Suppl 1):192. PubMed ID: 33297952
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Image Augmentation based on Variational Autoencoder for Breast Tumor Segmentation.
Balaji K
Acad Radiol; 2023 Sep; 30 Suppl 2():S172-S183. PubMed ID: 36804294
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks.
Baccouche A; Garcia-Zapirain B; Elmaghraby AS
Sci Rep; 2022 Jul; 12(1):12259. PubMed ID: 35851592
[TBL] [Abstract][Full Text] [Related]
9. TrEnD: A transformer-based encoder-decoder model with adaptive patch embedding for mass segmentation in mammograms.
Liu D; Wu B; Li C; Sun Z; Zhang N
Med Phys; 2023 May; 50(5):2884-2899. PubMed ID: 36609788
[TBL] [Abstract][Full Text] [Related]
10. Breast cancer detection using deep convolutional neural networks and support vector machines.
Ragab DA; Sharkas M; Marshall S; Ren J
PeerJ; 2019; 7():e6201. PubMed ID: 30713814
[TBL] [Abstract][Full Text] [Related]
11. A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.
Liu Y; Zhang M; Zhong Z; Zeng X
Med Phys; 2023 Mar; 50(3):1528-1538. PubMed ID: 36057788
[TBL] [Abstract][Full Text] [Related]
12. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images.
Rahman T; Khandakar A; Qiblawey Y; Tahir A; Kiranyaz S; Abul Kashem SB; Islam MT; Al Maadeed S; Zughaier SM; Khan MS; Chowdhury MEH
Comput Biol Med; 2021 May; 132():104319. PubMed ID: 33799220
[TBL] [Abstract][Full Text] [Related]
13. Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning.
Guo X; Zhang N; Guo J; Zhang H; Hao Y; Hang J
Med Phys; 2019 Dec; 46(12):5666-5676. PubMed ID: 31610020
[TBL] [Abstract][Full Text] [Related]
14. SVD-CLAHE boosting and balanced loss function for Covid-19 detection from an imbalanced Chest X-Ray dataset.
Roy S; Tyagi M; Bansal V; Jain V
Comput Biol Med; 2022 Nov; 150():106092. PubMed ID: 36208598
[TBL] [Abstract][Full Text] [Related]
15. A divide and conquer approach to maximise deep learning mammography classification accuracies.
Jaamour A; Myles C; Patel A; Chen SJ; McMillan L; Harris-Birtill D
PLoS One; 2023; 18(5):e0280841. PubMed ID: 37235566
[TBL] [Abstract][Full Text] [Related]
16. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.
Al-Antari MA; Al-Masni MA; Choi MT; Han SM; Kim TS
Int J Med Inform; 2018 Sep; 117():44-54. PubMed ID: 30032964
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. FS-UNet: Mass segmentation in mammograms using an encoder-decoder architecture with feature strengthening.
Pi J; Qi Y; Lou M; Li X; Wang Y; Xu C; Ma Y
Comput Biol Med; 2021 Oct; 137():104800. PubMed ID: 34507155
[TBL] [Abstract][Full Text] [Related]
19. A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica.
Calderon-Ramirez S; Murillo-Hernandez D; Rojas-Salazar K; Elizondo D; Yang S; Moemeni A; Molina-Cabello M
Med Biol Eng Comput; 2022 Apr; 60(4):1159-1175. PubMed ID: 35239108
[TBL] [Abstract][Full Text] [Related]
20. Whole mammographic mass segmentation using attention mechanism and multiscale pooling adversarial network.
Wang Y; Wang S; Chen J; Wu C
J Med Imaging (Bellingham); 2020 Sep; 7(5):054503. PubMed ID: 33102621
[No Abstract] [Full Text] [Related]
[Next] [New Search]
