Sparse Coding Induced Transfer learning for HEp-2 Cell Classification
Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 2nd International Conference on Biomedical Engineering and Biotechnology, 11–13 October 2013, Wuhan, China
Affiliations: School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China. E-mail: {liuanan,ytsu,yangzhx}@tju.edu.cn | Laboratory of Computer Vision and System, Tianjin University of Technology, Tianjin 300191, China. E-mail: [email protected] | College of Life Sciences, Tianjin Normal University, Tianjin 300387, China. E-mail: [email protected]
Note: [] This work was supported in part by the National Natural Science Foundation of China (61100124, 21106095, 61170239, 61202168) and the Foundation of Introducing Talents to Tianjin Normal University (5RL123).
Abstract: Automated human larynx carcinoma (HEp-2) cell classification is critical for medical diagnosis. In this paper, we propose a sparse coding-based unsupervised transfer learning method for HEp-2 cell classification. First, the low level image feature is extracted for visual representation. Second, a sparse coding scheme with the Elastic Net penalized convex objective function is proposed for unsupervised feature learning. At last, a Support Vector Machine classifier is utilized for model learning and predicting. To our knowledge, this work is the first to transfer the human-crafted visual feature, sensitive to the variation of appearance and shape during cell movement, to the high level representation which directly denotes the correlation of one sample and the bases in the learnt dictionary. Therefore, the proposed method can overcome the difficulty in discriminative feature formulation for different kinds of cells with irregular and changing visual patterns. Large scale comparison experiments will be conducted to show the superiority of this method.
Keywords: HEp-2 cell, cell classification, transfer learning, sparse coding, elastic net
