537 related articles for article (PubMed ID: 31228956)
21. Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound.
Häberle L; Hack CC; Heusinger K; Wagner F; Jud SM; Uder M; Beckmann MW; Schulz-Wendtland R; Wittenberg T; Fasching PA
Eur J Med Res; 2017 Aug; 22(1):30. PubMed ID: 28854966
[TBL] [Abstract][Full Text] [Related]
22. Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.
Squires S; Harkness EF; Mackenzie A; Evans DG; Howell SJ; Astley SM
Biomed Phys Eng Express; 2024 May; 10(4):. PubMed ID: 38701765
[No Abstract] [Full Text] [Related]
23. 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]
24. Assessment of global and local region-based bilateral mammographic feature asymmetry to predict short-term breast cancer risk.
Li Y; Fan M; Cheng H; Zhang P; Zheng B; Li L
Phys Med Biol; 2018 Jan; 63(2):025004. PubMed ID: 29226849
[TBL] [Abstract][Full Text] [Related]
25. Assessment of a fully automated, high-throughput mammographic density measurement tool for use with processed digital mammograms.
Couwenberg AM; Verkooijen HM; Li J; Pijnappel RM; Charaghvandi KR; Hartman M; van Gils CH
Cancer Causes Control; 2014 Aug; 25(8):1037-43. PubMed ID: 24962023
[TBL] [Abstract][Full Text] [Related]
26. Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms.
Cai H; Huang Q; Rong W; Song Y; Li J; Wang J; Chen J; Li L
Comput Math Methods Med; 2019; 2019():2717454. PubMed ID: 30944574
[TBL] [Abstract][Full Text] [Related]
27. A deep learning framework to classify breast density with noisy labels regularization.
Lopez-Almazan H; Javier Pérez-Benito F; Larroza A; Perez-Cortes JC; Pollan M; Perez-Gomez B; Salas Trejo D; Casals M; Llobet R
Comput Methods Programs Biomed; 2022 Jun; 221():106885. PubMed ID: 35594581
[TBL] [Abstract][Full Text] [Related]
28. 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]
29. 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]
30. Can a Machine Learn from Radiologists' Visual Search Behaviour and Their Interpretation of Mammograms-a Deep-Learning Study.
Mall S; Brennan PC; Mello-Thoms C
J Digit Imaging; 2019 Oct; 32(5):746-760. PubMed ID: 31410677
[TBL] [Abstract][Full Text] [Related]
31. Deep feature-based automatic classification of mammograms.
Arora R; Rai PK; Raman B
Med Biol Eng Comput; 2020 Jun; 58(6):1199-1211. PubMed ID: 32200453
[TBL] [Abstract][Full Text] [Related]
32. Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.
Al-Antari MA; Al-Masni MA; Kim TS
Adv Exp Med Biol; 2020; 1213():59-72. PubMed ID: 32030663
[TBL] [Abstract][Full Text] [Related]
33. 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]
34. Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS.
Jeffers AM; Sieh W; Lipson JA; Rothstein JH; McGuire V; Whittemore AS; Rubin DL
Radiology; 2017 Feb; 282(2):348-355. PubMed ID: 27598536
[TBL] [Abstract][Full Text] [Related]
35. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening.
Liu H; Chen Y; Zhang Y; Wang L; Luo R; Wu H; Wu C; Zhang H; Tan W; Yin H; Wang D
Eur Radiol; 2021 Aug; 31(8):5902-5912. PubMed ID: 33496829
[TBL] [Abstract][Full Text] [Related]
36. 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]
37. 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]
38. A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms.
Sun W; Tseng TB; Qian W; Saltzstein EC; Zheng B; Yu H; Zhou S
Comput Methods Programs Biomed; 2018 Mar; 155():29-38. PubMed ID: 29512502
[TBL] [Abstract][Full Text] [Related]
39. Multitask deep learning on mammography to predict extensive intraductal component in invasive breast cancer.
Tsai HY; Kao YW; Wang JC; Tsai TY; Chung WS; Hsu JS; Hou MF; Weng SF
Eur Radiol; 2024 Apr; 34(4):2593-2604. PubMed ID: 37812297
[TBL] [Abstract][Full Text] [Related]
40.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Previous] [Next] [New Search]
