BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

192 related articles for article (PubMed ID: 9608931)

  • 1. Detection of suspected malignant patterns in three-dimensional magnetic resonance breast images.
    el-Kwae EA; Fishman JE; Bianchi MJ; Pattany PM; Kabuka MR
    J Digit Imaging; 1998 May; 11(2):83-93. PubMed ID: 9608931
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A preliminary study on computerized lesion localization in MR mammography using 3D nMITR maps, multilayer cellular neural networks, and fuzzy c-partitioning.
    Ertas G; Gulcur HO; Tunaci M; Osman O; Ucan ON
    Med Phys; 2008 Jan; 35(1):195-205. PubMed ID: 18293575
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Labeling of MR brain images using Boolean neural network.
    Li X; Bhide S; Kabuka MR
    IEEE Trans Med Imaging; 1996; 15(5):628-38. PubMed ID: 18215944
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A mathematical theory of shape and neuro-fuzzy methodology-based diagnostic analysis: a comparative study on early detection and treatment planning of brain cancer.
    Kar S; Majumder DD
    Int J Clin Oncol; 2017 Aug; 22(4):667-681. PubMed ID: 28321787
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.
    Samala RK; Chan HP; Hadjiiski L; Helvie MA; Wei J; Cha K
    Med Phys; 2016 Dec; 43(12):6654. PubMed ID: 27908154
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Constructing a fuzzy rule-based system using the ILFN network and Genetic Algorithm.
    Yen GG; Meesad P
    Int J Neural Syst; 2001 Oct; 11(5):427-43. PubMed ID: 11709810
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.
    Treder M; Lauermann JL; Eter N
    Graefes Arch Clin Exp Ophthalmol; 2018 Nov; 256(11):2053-2060. PubMed ID: 30091055
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identification of brain regions responsible for Alzheimer's disease using a Self-adaptive Resource Allocation Network.
    Mahanand BS; Suresh S; Sundararajan N; Aswatha Kumar M
    Neural Netw; 2012 Aug; 32():313-22. PubMed ID: 22391013
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Multilevel analysis of spatiotemporal association features for differentiation of tumor enhancement patterns in breast DCE-MRI.
    Lee SH; Kim JH; Cho N; Park JS; Yang Z; Jung YS; Moon WK
    Med Phys; 2010 Aug; 37(8):3940-56. PubMed ID: 20879557
    [TBL] [Abstract][Full Text] [Related]  

  • 11. The geometrical learning of binary neural networks.
    Kim JH; Park SK
    IEEE Trans Neural Netw; 1995; 6(1):237-47. PubMed ID: 18263303
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms.
    Verma B
    Artif Intell Med; 2008 Jan; 42(1):67-79. PubMed ID: 17997084
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses.
    Hadjiiski L; Sahiner B; Chan HP; Petrick N; Helvie MA; Gurcan M
    Med Phys; 2001 Nov; 28(11):2309-17. PubMed ID: 11764038
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: a systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database.
    Dietzel M; Baltzer PA; Dietzel A; Zoubi R; Gröschel T; Burmeister HP; Bogdan M; Kaiser WA
    Eur J Radiol; 2012 Jul; 81(7):1508-13. PubMed ID: 21459533
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences.
    Noguchi T; Higa D; Asada T; Kawata Y; Machitori A; Shida Y; Okafuji T; Yokoyama K; Uchiyama F; Tajima T
    Jpn J Radiol; 2018 Dec; 36(12):691-697. PubMed ID: 30232585
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A use of a neural network to evaluate contrast enhancement curves in breast magnetic resonance images.
    Vergnaghi D; Monti A; Setti E; Musumeci R
    J Digit Imaging; 2001 Jun; 14(2 Suppl 1):58-9. PubMed ID: 11442122
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Benign/malignant classifier of soft tissue tumors using MR imaging.
    García-Gómez JM; Vidal C; Martí-Bonmatí L; Galant J; Sans N; Robles M; Casacuberta F
    MAGMA; 2004 Mar; 16(4):194-201. PubMed ID: 14999563
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images.
    Juntu J; Sijbers J; De Backer S; Rajan J; Van Dyck D
    J Magn Reson Imaging; 2010 Mar; 31(3):680-9. PubMed ID: 20187212
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography.
    Vomweg TW; Buscema M; Kauczor HU; Teifke A; Intraligi M; Terzi S; Heussel CP; Achenbach T; Rieker O; Mayer D; Thelen M
    Med Phys; 2003 Sep; 30(9):2350-9. PubMed ID: 14528957
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.
    Pang S; Yu Z; Orgun MA
    Comput Methods Programs Biomed; 2017 Mar; 140():283-293. PubMed ID: 28254085
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.