BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

447 related articles for article (PubMed ID: 28436405)

  • 21. Computer aided detection system for micro calcifications in digital mammograms.
    Mohamed H; Mabrouk MS; Sharawy A
    Comput Methods Programs Biomed; 2014 Oct; 116(3):226-35. PubMed ID: 24909786
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Multi-scaled morphological features for the characterization of mammographic masses using statistical classification schemes.
    Georgiou H; Mavroforakis M; Dimitropoulos N; Cavouras D; Theodoridis S
    Artif Intell Med; 2007 Sep; 41(1):39-55. PubMed ID: 17714924
    [TBL] [Abstract][Full Text] [Related]  

  • 23. 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]  

  • 24. Fissures segmentation using surface features: content-based retrieval for mammographic mass using ensemble classifier.
    Liu H; Lan Y; Xu X; Song E; Hung CC
    Acad Radiol; 2011 Dec; 18(12):1475-84. PubMed ID: 22055794
    [TBL] [Abstract][Full Text] [Related]  

  • 25. 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]  

  • 26. Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.
    Milenković J; Hertl K; Košir A; Zibert J; Tasič JF
    Artif Intell Med; 2013 Jun; 58(2):101-14. PubMed ID: 23548472
    [TBL] [Abstract][Full Text] [Related]  

  • 27. A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features.
    Melekoodappattu JG; Subbian PS
    J Med Syst; 2019 May; 43(7):183. PubMed ID: 31093789
    [TBL] [Abstract][Full Text] [Related]  

  • 28. A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images.
    Wei M; Du Y; Wu X; Su Q; Zhu J; Zheng L; Lv G; Zhuang J
    Comput Math Methods Med; 2020; 2020():5894010. PubMed ID: 33062038
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.
    Jebamony J; Jacob D
    Curr Med Imaging; 2020; 16(6):703-710. PubMed ID: 32723242
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis.
    Karahaliou A; Skiadopoulos S; Boniatis I; Sakellaropoulos P; Likaki E; Panayiotakis G; Costaridou L
    Br J Radiol; 2007 Aug; 80(956):648-56. PubMed ID: 17621604
    [TBL] [Abstract][Full Text] [Related]  

  • 31. A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: comparison to a Bayesian classifier.
    Chang Y; Lim J; Kim N; Seo JB; Lynch DA
    Med Phys; 2013 May; 40(5):051912. PubMed ID: 23635282
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Breast Cancer Diagnosis in Digital Mammography Images Using Automatic Detection for the Region of Interest.
    Ramadan SZ; El-Banna M
    Curr Med Imaging; 2020; 16(7):902-912. PubMed ID: 33059560
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests.
    Singh VP; Srivastava S; Srivastava R
    Technol Health Care; 2017 Aug; 25(4):709-727. PubMed ID: 28582938
    [TBL] [Abstract][Full Text] [Related]  

  • 34. A computer-aided diagnosis scheme of breast lesion classification using GLGLM and shape features: Combined-view and multi-classifiers.
    Liang C; Bian Z; Lv W; Chen S; Zeng D; Ma J
    Phys Med; 2018 Nov; 55():61-72. PubMed ID: 30471821
    [TBL] [Abstract][Full Text] [Related]  

  • 35. SVM based system for classification of microcalcifications in digital mammograms.
    Singh S; Kumar V; Verma HK; Singh D
    Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():4747-50. PubMed ID: 17945853
    [TBL] [Abstract][Full Text] [Related]  

  • 36. False Positive Reduction by an Annular Model as a Set of Few Features for Microcalcification Detection to Assist Early Diagnosis of Breast Cancer.
    Hernández-Capistrán J; Martínez-Carballido JF; Rosas-Romero R
    J Med Syst; 2018 Jun; 42(8):134. PubMed ID: 29915992
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Breast mass classification on mammograms using radial local ternary patterns.
    Muramatsu C; Hara T; Endo T; Fujita H
    Comput Biol Med; 2016 May; 72():43-53. PubMed ID: 27015322
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Ant-cuckoo colony optimization for feature selection in digital mammogram.
    Jona JB; Nagaveni N
    Pak J Biol Sci; 2014 Jan; 17(2):266-71. PubMed ID: 24783812
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Improving the Mann-Whitney statistical test for feature selection: an approach in breast cancer diagnosis on mammography.
    Pérez NP; Guevara López MA; Silva A; Ramos I
    Artif Intell Med; 2015 Jan; 63(1):19-31. PubMed ID: 25555756
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Using multiscale texture and density features for near-term breast cancer risk analysis.
    Sun W; Tseng TL; Qian W; Zhang J; Saltzstein EC; Zheng B; Lure F; Yu H; Zhou S
    Med Phys; 2015 Jun; 42(6):2853-62. PubMed ID: 26127038
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 23.