Sparse Coding Induced Transfer learning for HEp-2 Cell Classification

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.