1318 related articles for article (PubMed ID: 30771257)
41. A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms.
Shi P; Zhong J; Rampun A; Wang H
Comput Biol Med; 2018 May; 96():178-188. PubMed ID: 29597143
[TBL] [Abstract][Full Text] [Related]
42. Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI.
Song Y; Zhang YD; Yan X; Liu H; Zhou M; Hu B; Yang G
J Magn Reson Imaging; 2018 Dec; 48(6):1570-1577. PubMed ID: 29659067
[TBL] [Abstract][Full Text] [Related]
43. Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis.
Samala RK; Chan HP; Hadjiiski LM; Helvie MA; Richter C; Cha K
Phys Med Biol; 2018 May; 63(9):095005. PubMed ID: 29616660
[TBL] [Abstract][Full Text] [Related]
44. MR-based synthetic CT generation using a deep convolutional neural network method.
Han X
Med Phys; 2017 Apr; 44(4):1408-1419. PubMed ID: 28192624
[TBL] [Abstract][Full Text] [Related]
45. 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]
46. Removal of pectoral muscle based on topographic map and shape-shifting silhouette.
Mughal B; Muhammad N; Sharif M; Rehman A; Saba T
BMC Cancer; 2018 Aug; 18(1):778. PubMed ID: 30068304
[TBL] [Abstract][Full Text] [Related]
47. Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection.
Shen R; Yan K; Xiao F; Chang J; Jiang C; Zhou K
J Digit Imaging; 2018 Oct; 31(5):680-691. PubMed ID: 29582242
[TBL] [Abstract][Full Text] [Related]
48. Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.
Ciritsis A; Rossi C; Eberhard M; Marcon M; Becker AS; Boss A
Eur Radiol; 2019 Oct; 29(10):5458-5468. PubMed ID: 30927100
[TBL] [Abstract][Full Text] [Related]
49. Quality control system for mammographic breast positioning using deep learning.
Watanabe H; Hayashi S; Kondo Y; Matsuyama E; Hayashi N; Ogura T; Shimosegawa M
Sci Rep; 2023 May; 13(1):7066. PubMed ID: 37127674
[TBL] [Abstract][Full Text] [Related]
50. Standardised or individualised X-ray tube angle for mediolateral oblique projection in digital mammography?
Moshina N; Bjørnson EW; Holen ÅS; Larsen M; Hansestad B; Tøsdal L; Hofvind S
Radiography (Lond); 2022 Aug; 28(3):772-778. PubMed ID: 35387753
[TBL] [Abstract][Full Text] [Related]
51. 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]
52. 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]
53. A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry.
Tzikopoulos SD; Mavroforakis ME; Georgiou HV; Dimitropoulos N; Theodoridis S
Comput Methods Programs Biomed; 2011 Apr; 102(1):47-63. PubMed ID: 21306782
[TBL] [Abstract][Full Text] [Related]
54. A New Breast Border Extraction and Contrast Enhancement Technique with Digital Mammogram Images for Improved Detection of Breast Cancer.
Hazarika M; Mahanta LB
Asian Pac J Cancer Prev; 2018 Aug; 19(8):2141-2148. PubMed ID: 30139217
[TBL] [Abstract][Full Text] [Related]
55. Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning.
Gudhe NR; Behravan H; Sudah M; Okuma H; Vanninen R; Kosma VM; Mannermaa A
Sci Rep; 2022 Jul; 12(1):12060. PubMed ID: 35835933
[TBL] [Abstract][Full Text] [Related]
56. A breast density index for digital mammograms based on radiologists' ranking.
Boone JM; Lindfors KK; Beatty CS; Seibert JA
J Digit Imaging; 1998 Aug; 11(3):101-15. PubMed ID: 9718500
[TBL] [Abstract][Full Text] [Related]
57. 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]
58. Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms.
Mustra M; Grgic M; Rangayyan RM
Med Biol Eng Comput; 2016 Jul; 54(7):1003-24. PubMed ID: 26546074
[TBL] [Abstract][Full Text] [Related]
59. 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]
60. 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]
[Previous] [Next] [New Search]
