415 related articles for article (PubMed ID: 31249497)
1. An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images.
Duggento A; Aiello M; Cavaliere C; Cascella GL; Cascella D; Conte G; Guerrisi M; Toschi N
Contrast Media Mol Imaging; 2019; 2019():5982834. PubMed ID: 31249497
[TBL] [Abstract][Full Text] [Related]
2. A random initialization deep neural network for discriminating malignant breast cancer lesions.
Duggento A; Guerrisi M; Toschi N; Scimeca M; Urbano N; Bonanno E; Aiello M; Cavaliere C; Cascella GL; Cascella D; Conte G
Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():912-915. PubMed ID: 31946042
[TBL] [Abstract][Full Text] [Related]
3. Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective.
Mohamed AA; Luo Y; Peng H; Jankowitz RC; Wu S
J Digit Imaging; 2018 Aug; 31(4):387-392. PubMed ID: 28932980
[TBL] [Abstract][Full Text] [Related]
4. Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer.
Becker AS; Marcon M; Ghafoor S; Wurnig MC; Frauenfelder T; Boss A
Invest Radiol; 2017 Jul; 52(7):434-440. PubMed ID: 28212138
[TBL] [Abstract][Full Text] [Related]
5. Deep Convolutional Neural Networks for breast cancer screening.
Chougrad H; Zouaki H; Alheyane O
Comput Methods Programs Biomed; 2018 Apr; 157():19-30. PubMed ID: 29477427
[TBL] [Abstract][Full Text] [Related]
6. Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.
Kooi T; van Ginneken B; Karssemeijer N; den Heeten A
Med Phys; 2017 Mar; 44(3):1017-1027. PubMed ID: 28094850
[TBL] [Abstract][Full Text] [Related]
7. Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction.
Dembrower K; Liu Y; Azizpour H; Eklund M; Smith K; Lindholm P; Strand F
Radiology; 2020 Feb; 294(2):265-272. PubMed ID: 31845842
[TBL] [Abstract][Full Text] [Related]
8. Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN).
Agnes SA; Anitha J; Pandian SIA; Peter JD
J Med Syst; 2019 Dec; 44(1):30. PubMed ID: 31838610
[TBL] [Abstract][Full Text] [Related]
9. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.
Rodriguez-Ruiz A; Lång K; Gubern-Merida A; Teuwen J; Broeders M; Gennaro G; Clauser P; Helbich TH; Chevalier M; Mertelmeier T; Wallis MG; Andersson I; Zackrisson S; Sechopoulos I; Mann RM
Eur Radiol; 2019 Sep; 29(9):4825-4832. PubMed ID: 30993432
[TBL] [Abstract][Full Text] [Related]
10. Large scale deep learning for computer aided detection of mammographic lesions.
Kooi T; Litjens G; van Ginneken B; Gubern-Mérida A; Sánchez CI; Mann R; den Heeten A; Karssemeijer N
Med Image Anal; 2017 Jan; 35():303-312. PubMed ID: 27497072
[TBL] [Abstract][Full Text] [Related]
11. Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.
Hinton B; Ma L; Mahmoudzadeh AP; Malkov S; Fan B; Greenwood H; Joe B; Lee V; Kerlikowske K; Shepherd J
Cancer Imaging; 2019 Jun; 19(1):41. PubMed ID: 31228956
[TBL] [Abstract][Full Text] [Related]
12. Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks.
Yi PH; Lin A; Wei J; Yu AC; Sair HI; Hui FK; Hager GD; Harvey SC
J Digit Imaging; 2019 Aug; 32(4):565-570. PubMed ID: 31197559
[TBL] [Abstract][Full Text] [Related]
13. Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.
Bandeira Diniz JO; Bandeira Diniz PH; Azevedo Valente TL; Corrêa Silva A; de Paiva AC; Gattass M
Comput Methods Programs Biomed; 2018 Mar; 156():191-207. PubMed ID: 29428071
[TBL] [Abstract][Full Text] [Related]
14. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.
Rodríguez-Ruiz A; Krupinski E; Mordang JJ; Schilling K; Heywang-Köbrunner SH; Sechopoulos I; Mann RM
Radiology; 2019 Feb; 290(2):305-314. PubMed ID: 30457482
[TBL] [Abstract][Full Text] [Related]
15. Deep convolutional neural networks for mammography: advances, challenges and applications.
Abdelhafiz D; Yang C; Ammar R; Nabavi S
BMC Bioinformatics; 2019 Jun; 20(Suppl 11):281. PubMed ID: 31167642
[TBL] [Abstract][Full Text] [Related]
16. Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network.
Fujioka T; Kubota K; Mori M; Kikuchi Y; Katsuta L; Kasahara M; Oda G; Ishiba T; Nakagawa T; Tateishi U
Jpn J Radiol; 2019 Jun; 37(6):466-472. PubMed ID: 30888570
[TBL] [Abstract][Full Text] [Related]
17. Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network.
Alom MZ; Yakopcic C; Nasrin MS; Taha TM; Asari VK
J Digit Imaging; 2019 Aug; 32(4):605-617. PubMed ID: 30756265
[TBL] [Abstract][Full Text] [Related]
18. Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.
Li S; Wei J; Chan HP; Helvie MA; Roubidoux MA; Lu Y; Zhou C; Hadjiiski LM; Samala RK
Phys Med Biol; 2018 Jan; 63(2):025005. PubMed ID: 29210358
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Deep learning for detection of iso-dense, obscure masses in mammographically dense breasts.
Rangarajan K; Aggarwal P; Gupta DK; Dhanakshirur R; Baby A; Pal C; Gupta AK; Hari S; Banerjee S; Arora C
Eur Radiol; 2023 Nov; 33(11):8112-8121. PubMed ID: 37209125
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
