Discovering discriminative cell attributes for HEp-2 specimen image classification
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[1] Antonio Torralba,et al. Spectral Hashing , 2008, NIPS.
[2] A. Wiik,et al. Antinuclear antibodies: a contemporary nomenclature using HEp-2 cells. , 2010, Journal of autoimmunity.
[3] Mario Vento,et al. Benchmarking HEp-2 Cells Classification Methods , 2013, IEEE Transactions on Medical Imaging.
[4] Andrew Zisserman,et al. Learning Visual Attributes , 2007, NIPS.
[5] Yongkang Wong,et al. Classification of Human Epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).
[6] Christoph H. Lampert,et al. Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[7] Andrew Zisserman,et al. Efficient additive kernels via explicit feature maps , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[8] Yan Yang,et al. Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns , 2014, Pattern Recognit..
[9] P. Schur,et al. ANA screening: an old test with new recommendations , 2010, Annals of the rheumatic diseases.
[10] Andrew W. Fitzgibbon,et al. PiCoDes: Learning a Compact Code for Novel-Category Recognition , 2011, NIPS.
[11] Ali Farhadi,et al. Attribute Discovery via Predictable Discriminative Binary Codes , 2012, ECCV.
[12] Brian C. Lovell,et al. Fisher tensors for classifying human epithelial cells , 2014, Pattern Recognit..
[13] B. Pham,et al. Impact of external quality assessment on antinuclear antibody detection performance , 2005, Lupus.
[14] Kristen Grauman,et al. Relative attributes , 2011, 2011 International Conference on Computer Vision.
[15] Brian C. Lovell,et al. An Automatic Image Based Single Dilution Method for End Point Titre Quantitation of Antinuclear Antibodies Tests Using HEp-2 Cells , 2011, 2011 International Conference on Digital Image Computing: Techniques and Applications.
[16] Hao Su,et al. Object Bank: An Object-Level Image Representation for High-Level Visual Recognition , 2014, International Journal of Computer Vision.
[17] Moses Charikar,et al. Similarity estimation techniques from rounding algorithms , 2002, STOC '02.
[18] N. Bizzaro,et al. Variability between methods to determine ANA, anti-dsDNA and anti-ENA autoantibodies: a collaborative study with the biomedical industry. , 1998, Journal of immunological methods.
[19] Svetlana Lazebnik,et al. Iterative quantization: A procrustean approach to learning binary codes , 2011, CVPR 2011.
[20] Yongkang Wong,et al. Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching , 2014, bioRxiv.
[21] Kristen Grauman,et al. Kernelized locality-sensitive hashing for scalable image search , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[22] Paolo Soda,et al. Color to grayscale staining pattern representation in IIF , 2011, 2011 24th International Symposium on Computer-Based Medical Systems (CBMS).
[23] Giulio Iannello,et al. Aggregation of Classifiers for Staining Pattern Recognition in Antinuclear Autoantibodies Analysis , 2009, IEEE Transactions on Information Technology in Biomedicine.
[24] Vincenzo Piuri,et al. All-IDB: The acute lymphoblastic leukemia image database for image processing , 2011, 2011 18th IEEE International Conference on Image Processing.