@InProceedings{7760431,
  author = {S. Monajemi and S. Ensafi and S. Lu and A. A. Kassim and C. L. Tan and S. Sanei and S. Ong},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {Classification of HEp-2 cells using distributed dictionary learning},
  year = {2016},
  pages = {1163-1167},
  abstract = {Automatic classification of human epithelial type-2 (HEp-2) cells can improve the diagnostic process of autoimmune diseases (ADs) in terms of lower cost, faster response, and better repeatability. However, most of the proposed methods for classification of HEp-2 cells suffer from several constraints including tedious parameter tuning, massive memory requirement, and high computational costs. We propose an adaptive distributed dictionary learning (ADDL) method where the dictionary learning problem is reformulated as a distributed learning task. With the help of this approach, we develop an automatic and robust method that effectively handles the complexity of the problem in terms of memory and computational cost and also obtains superior classification accuracy.},
  keywords = {cellular biophysics;diseases;learning (artificial intelligence);medical diagnostic computing;patient diagnosis;HEp-2 cell classification;automatic classification;human epithelial type-2 cells;diagnostic process;autoimmune diseases;adaptive distributed dictionary learning method;ADDL;dictionary learning problem;classification accuracy;Dictionaries;Feature extraction;Cost function;Europe;Signal processing;Memory management},
  doi = {10.1109/EUSIPCO.2016.7760431},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570255602.pdf},
}

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L.","Sanei, S.","Ong, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["S."],"propositions":[],"lastnames":["Monajemi"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Ensafi"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Lu"],"suffixes":[]},{"firstnames":["A.","A."],"propositions":[],"lastnames":["Kassim"],"suffixes":[]},{"firstnames":["C.","L."],"propositions":[],"lastnames":["Tan"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Sanei"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Ong"],"suffixes":[]}],"booktitle":"2016 24th European Signal Processing Conference (EUSIPCO)","title":"Classification of HEp-2 cells using distributed dictionary learning","year":"2016","pages":"1163-1167","abstract":"Automatic classification of human epithelial type-2 (HEp-2) cells can improve the diagnostic process of autoimmune diseases (ADs) in terms of lower cost, faster response, and better repeatability. However, most of the proposed methods for classification of HEp-2 cells suffer from several constraints including tedious parameter tuning, massive memory requirement, and high computational costs. We propose an adaptive distributed dictionary learning (ADDL) method where the dictionary learning problem is reformulated as a distributed learning task. 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Ong},\n booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},\n title = {Classification of HEp-2 cells using distributed dictionary learning},\n year = {2016},\n pages = {1163-1167},\n abstract = {Automatic classification of human epithelial type-2 (HEp-2) cells can improve the diagnostic process of autoimmune diseases (ADs) in terms of lower cost, faster response, and better repeatability. However, most of the proposed methods for classification of HEp-2 cells suffer from several constraints including tedious parameter tuning, massive memory requirement, and high computational costs. We propose an adaptive distributed dictionary learning (ADDL) method where the dictionary learning problem is reformulated as a distributed learning task. 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