119 related articles for article (PubMed ID: 37698346)
21. Computer-aided detection of clustered microcalcifications in multiscale bilateral filtering regularized reconstructed digital breast tomosynthesis volume.
Samala RK; Chan HP; Lu Y; Hadjiiski L; Wei J; Sahiner B; Helvie MA
Med Phys; 2014 Feb; 41(2):021901. PubMed ID: 24506622
[TBL] [Abstract][Full Text] [Related]
22. Quantification of Al-equivalent thickness of just visible microcalcifications in full field digital mammograms.
Carton AK; Bosmans H; Vandenbroucke D; Souverijns G; Van Ongeval C; Dragusin O; Marchal G
Med Phys; 2004 Jul; 31(7):2165-76. PubMed ID: 15305471
[TBL] [Abstract][Full Text] [Related]
23. A Micro CT Study in Patients with Breast Microcalcifications Using a Mathematical Algorithm to Assess 3D Structure.
Kenkel D; Varga Z; Heuer H; Dedes KJ; Berger N; Filli L; Boss A
PLoS One; 2017; 12(1):e0169349. PubMed ID: 28107436
[TBL] [Abstract][Full Text] [Related]
24. Microcalcification detectability in breast CT images using CNN observers.
Lyu SH; Abbey CK; Hernandez AM; Boone JM
Med Phys; 2024 Feb; 51(2):933-945. PubMed ID: 38154070
[TBL] [Abstract][Full Text] [Related]
25. Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach.
Kermouni Serradj N; Messadi M; Lazzouni S
J Digit Imaging; 2022 Dec; 35(6):1544-1559. PubMed ID: 35854037
[TBL] [Abstract][Full Text] [Related]
26. A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images.
Sarvestani ZM; Jamali J; Taghizadeh M; Dindarloo MHF
J Cancer Res Clin Oncol; 2023 Aug; 149(9):6151-6170. PubMed ID: 36680580
[TBL] [Abstract][Full Text] [Related]
27. Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network.
Rehman KU; Li J; Pei Y; Yasin A; Ali S; Mahmood T
Sensors (Basel); 2021 Jul; 21(14):. PubMed ID: 34300597
[TBL] [Abstract][Full Text] [Related]
28. Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images.
Chan HP; Helvie MA; Gao M; Hadjiiski L; Zhou C; Garver K; Klein KA; McLaughlin C; Oudsema R; Rahman WT; Roubidoux MA
Med Phys; 2023 Oct; 50(10):6177-6189. PubMed ID: 37145996
[TBL] [Abstract][Full Text] [Related]
29. A Mammography-Based Radiomic Nomogram for Predicting Malignancy in Breast Suspicious Microcalcifications.
Chen Y; Jiang H; Li J; Zhang J; Wu P; Dai Z
Acad Radiol; 2024 Feb; 31(2):492-502. PubMed ID: 37940427
[TBL] [Abstract][Full Text] [Related]
30. A swarm optimized neural network system for classification of microcalcification in mammograms.
Dheeba J; Selvi ST
J Med Syst; 2012 Oct; 36(5):3051-61. PubMed ID: 21947904
[TBL] [Abstract][Full Text] [Related]
31. A new approach for clustered MCs classification with sparse features learning and TWSVM.
Zhang XS
ScientificWorldJournal; 2014; 2014():970287. PubMed ID: 24764773
[TBL] [Abstract][Full Text] [Related]
32. Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach.
Gao M; Fessler JA; Chan HP
Phys Med Biol; 2023 Dec; 68(24):. PubMed ID: 37988758
[No Abstract] [Full Text] [Related]
33. Computerized evaluation of mammographic lesions: what diagnostic role does the shape of the individual microcalcifications play compared with the geometry of the cluster?
Leichter I; Lederman R; Buchbinder SS; Bamberger P; Novak B; Fields S
AJR Am J Roentgenol; 2004 Mar; 182(3):705-12. PubMed ID: 14975973
[TBL] [Abstract][Full Text] [Related]
34. [Report on the 89th Scientific Assembly and Annual Meeting of the Radiological Society of North America--micro-focus x-ray CT imaging of breast specimens with microcalcifications].
Nishide H; Kasuga T; Miyachi T
Nihon Hoshasen Gijutsu Gakkai Zasshi; 2004 Dec; 60(12):1662-3. PubMed ID: 15614212
[TBL] [Abstract][Full Text] [Related]
35. Digital mammography: observer performance study of the effects of pixel size on the characterization of malignant and benign microcalcifications.
Chan HP; Helvie MA; Petrick N; Sahiner B; Adler DD; Paramagul C; Roubidoux MA; Blane CE; Joynt LK; Wilson TE; Hadjiiski LM; Goodsitt MM
Acad Radiol; 2001 Jun; 8(6):454-66. PubMed ID: 11394537
[TBL] [Abstract][Full Text] [Related]
36. Computer Aided Detection of Clustered Microcalcification: A Survey.
Kumar MNA; Kumar MNA; Sheshadri HS
Curr Med Imaging Rev; 2019; 15(2):132-149. PubMed ID: 31975660
[TBL] [Abstract][Full Text] [Related]
37. Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach.
Marasinou C; Li B; Paige J; Omigbodun A; Nakhaei N; Hoyt A; Hsu W
J Digit Imaging; 2023 Jun; 36(3):1016-1028. PubMed ID: 36820930
[TBL] [Abstract][Full Text] [Related]
38. Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model.
Wang J; Nishikawa RM; Yang Y
Med Phys; 2016 Jan; 43(1):159. PubMed ID: 26745908
[TBL] [Abstract][Full Text] [Related]
39. Analysis of computer-aided detection techniques and signal characteristics for clustered microcalcifications on digital mammography and digital breast tomosynthesis.
Samala RK; Chan HP; Hadjiiski LM; Helvie MA
Phys Med Biol; 2016 Oct; 61(19):7092-7112. PubMed ID: 27648708
[TBL] [Abstract][Full Text] [Related]
40. 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]
