Header Logo

Connection

Dimitris Metaxas to Sensitivity and Specificity

This is a "connection" page, showing publications Dimitris Metaxas has written about Sensitivity and Specificity.
Connection Strength

1.570
  1. Scalable histopathological image analysis via supervised hashing with multiple features. Med Image Anal. 2016 Dec; 34:3-12.
    View in: PubMed
    Score: 0.124
  2. An efficient conditional random field approach for automatic and interactive neuron segmentation. Med Image Anal. 2016 Jan; 27:31-44.
    View in: PubMed
    Score: 0.115
  3. Computer-aided diagnosis of mammographic masses using scalable image retrieval. IEEE Trans Biomed Eng. 2015 Feb; 62(2):783-92.
    View in: PubMed
    Score: 0.110
  4. On continuous user authentication via typing behavior. IEEE Trans Image Process. 2014 Oct; 23(10):4611-24.
    View in: PubMed
    Score: 0.109
  5. Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials. Comput Med Imaging Graph. 2015 Apr; 41:80-92.
    View in: PubMed
    Score: 0.107
  6. Scalable histopathological image analysis via active learning. Med Image Comput Comput Assist Interv. 2014; 17(Pt 3):369-76.
    View in: PubMed
    Score: 0.104
  7. Optree: a learning-based adaptive watershed algorithm for neuron segmentation. Med Image Comput Comput Assist Interv. 2014; 17(Pt 1):97-105.
    View in: PubMed
    Score: 0.104
  8. Collaborative multi organ segmentation by integrating deformable and graphical models. Med Image Comput Comput Assist Interv. 2013; 16(Pt 2):157-64.
    View in: PubMed
    Score: 0.097
  9. Deformable segmentation via sparse representation and dictionary learning. Med Image Anal. 2012 Oct; 16(7):1385-96.
    View in: PubMed
    Score: 0.094
  10. Simplified labeling process for medical image segmentation. Med Image Comput Comput Assist Interv. 2012; 15(Pt 2):387-94.
    View in: PubMed
    Score: 0.090
  11. Shape prior modeling using sparse representation and online dictionary learning. Med Image Comput Comput Assist Interv. 2012; 15(Pt 3):435-42.
    View in: PubMed
    Score: 0.090
  12. Efficient MR image reconstruction for compressed MR imaging. Med Image Comput Comput Assist Interv. 2010; 13(Pt 1):135-42.
    View in: PubMed
    Score: 0.079
  13. Lesion-specific coronary artery calcium quantification for predicting cardiac event with multiple instance support vector machines. Med Image Comput Comput Assist Interv. 2010; 13(Pt 1):484-92.
    View in: PubMed
    Score: 0.079
  14. CoCRF deformable model: a geometric model driven by collaborative conditional random fields. IEEE Trans Image Process. 2009 Oct; 18(10):2316-29.
    View in: PubMed
    Score: 0.076
  15. Bodypart Recognition Using Multi-stage Deep Learning. Inf Process Med Imaging. 2015; 24:449-61.
    View in: PubMed
    Score: 0.028
  16. A homotopy-based sparse representation for fast and accurate shape prior modeling in liver surgical planning. Med Image Anal. 2015 Jan; 19(1):176-86.
    View in: PubMed
    Score: 0.027
  17. Deformable models with sparsity constraints for cardiac motion analysis. Med Image Anal. 2014 Aug; 18(6):927-37.
    View in: PubMed
    Score: 0.026
  18. Segmenting the papillary muscles and the trabeculae from high resolution cardiac CT through restoration of topological handles. Inf Process Med Imaging. 2013; 23:184-95.
    View in: PubMed
    Score: 0.024
  19. Sparse deformable models with application to cardiac motion analysis. Inf Process Med Imaging. 2013; 23:208-19.
    View in: PubMed
    Score: 0.024
  20. Temporal shape analysis via the spectral signature. Med Image Comput Comput Assist Interv. 2012; 15(Pt 2):49-56.
    View in: PubMed
    Score: 0.023
  21. Towards robust and effective shape modeling: sparse shape composition. Med Image Anal. 2012 Jan; 16(1):265-77.
    View in: PubMed
    Score: 0.022
  22. A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy. Med Phys. 2010 Mar; 37(3):1298-308.
    View in: PubMed
    Score: 0.020
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.