228 related articles for article (PubMed ID: 29426948)
21. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.
Bien N; Rajpurkar P; Ball RL; Irvin J; Park A; Jones E; Bereket M; Patel BN; Yeom KW; Shpanskaya K; Halabi S; Zucker E; Fanton G; Amanatullah DF; Beaulieu CF; Riley GM; Stewart RJ; Blankenberg FG; Larson DB; Jones RH; Langlotz CP; Ng AY; Lungren MP
PLoS Med; 2018 Nov; 15(11):e1002699. PubMed ID: 30481176
[TBL] [Abstract][Full Text] [Related]
22. Impact of Transfer Learning Using Local Data on Performance of a Deep Learning Model for Screening Mammography.
Condon JJJ; Trinh V; Hall KA; Reintals M; Holmes AS; Oakden-Rayner L; Palmer LJ
Radiol Artif Intell; 2024 Jul; 6(4):e230383. PubMed ID: 38717291
[TBL] [Abstract][Full Text] [Related]
23. Lesion attention guided neural network for contrast-enhanced mammography-based biomarker status prediction in breast cancer.
Qian N; Jiang W; Wu X; Zhang N; Yu H; Guo Y
Comput Methods Programs Biomed; 2024 Jun; 250():108194. PubMed ID: 38678959
[TBL] [Abstract][Full Text] [Related]
24. 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]
25. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.
Al-Masni MA; Al-Antari MA; Park JM; Gi G; Kim TY; Rivera P; Valarezo E; Choi MT; Han SM; Kim TS
Comput Methods Programs Biomed; 2018 Apr; 157():85-94. PubMed ID: 29477437
[TBL] [Abstract][Full Text] [Related]
26. Deep Learning Pre-training Strategy for Mammogram Image Classification: an Evaluation Study.
Clancy K; Aboutalib S; Mohamed A; Sumkin J; Wu S
J Digit Imaging; 2020 Oct; 33(5):1257-1265. PubMed ID: 32607908
[TBL] [Abstract][Full Text] [Related]
27. Deep learning in mammography and breast histology, an overview and future trends.
Hamidinekoo A; Denton E; Rampun A; Honnor K; Zwiggelaar R
Med Image Anal; 2018 Jul; 47():45-67. PubMed ID: 29679847
[TBL] [Abstract][Full Text] [Related]
28. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.
Samala RK; Chan HP; Hadjiiski LM; Helvie MA; Cha KH; Richter CD
Phys Med Biol; 2017 Nov; 62(23):8894-8908. PubMed ID: 29035873
[TBL] [Abstract][Full Text] [Related]
29. 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]
30. 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]
31. Improved PAA algorithm for breast mass detection in mammograms.
Liu W; Zeng P; Jiang J; Chen J; Chen L; Hu C; Jian W; Diao X; Wang X
Comput Methods Programs Biomed; 2024 Jun; 251():108211. PubMed ID: 38744058
[TBL] [Abstract][Full Text] [Related]
32. [Establishment of a deep feature-based classification model for distinguishing benign and malignant breast tumors on full-filed digital mammography].
Liang C; Li M; Bian Z; Lv W; Zeng D; Ma J
Nan Fang Yi Ke Da Xue Xue Bao; 2019 Jan; 39(1):88-92. PubMed ID: 30692072
[TBL] [Abstract][Full Text] [Related]
33. Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images.
Chaves E; Gonçalves CB; Albertini MK; Lee S; Jeon G; Fernandes HC
Appl Opt; 2020 Jun; 59(17):E23-E28. PubMed ID: 32543509
[TBL] [Abstract][Full Text] [Related]
34. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.
Bickelhaupt S; Paech D; Kickingereder P; Steudle F; Lederer W; Daniel H; Götz M; Gählert N; Tichy D; Wiesenfarth M; Laun FB; Maier-Hein KH; Schlemmer HP; Bonekamp D
J Magn Reson Imaging; 2017 Aug; 46(2):604-616. PubMed ID: 28152264
[TBL] [Abstract][Full Text] [Related]
35. Mammography Image Quality Assurance Using Deep Learning.
Kretz T; Mueller KR; Schaeffter T; Elster C
IEEE Trans Biomed Eng; 2020 Dec; 67(12):3317-3326. PubMed ID: 32305886
[TBL] [Abstract][Full Text] [Related]
36. Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.
Patel BK; Ranjbar S; Wu T; Pockaj BA; Li J; Zhang N; Lobbes M; Zhang B; Mitchell JR
Eur J Radiol; 2018 Jan; 98():207-213. PubMed ID: 29279165
[TBL] [Abstract][Full Text] [Related]
37. Decision support system for breast cancer detection using mammograms.
Ganesan K; Acharya RU; Chua CK; Min LC; Mathew B; Thomas AK
Proc Inst Mech Eng H; 2013 Jul; 227(7):721-32. PubMed ID: 23636749
[TBL] [Abstract][Full Text] [Related]
38. 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]
39. Improving Workflow Efficiency for Mammography Using Machine Learning.
Kyono T; Gilbert FJ; van der Schaar M
J Am Coll Radiol; 2020 Jan; 17(1 Pt A):56-63. PubMed ID: 31153798
[TBL] [Abstract][Full Text] [Related]
40. Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning.
Zeiser FA; da Costa CA; Zonta T; Marques NMC; Roehe AV; Moreno M; da Rosa Righi R
J Digit Imaging; 2020 Aug; 33(4):858-868. PubMed ID: 32206943
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
