Cross domain mitotic cell recognition

Accurate and automated identification of mitosis is essential and challenging to many biomedical applications. To handle this challenge, we propose a novel mitotic cell recognition method by integrating heterogenous data in the framework of cross domain learning. First, we extract the discriminative feature to represent the local structure and textural saliency of individual cell sample. Second, the cell type-dependent classifiers are respectively trained on the target domain and the auxiliary domain and then fused in the framework of adaptive support vector machine for cross-domain learning. The achieved classifier can be implemented for mitotic cell recognition in the cross domain manner. The extensive experiments on two kinds of phase contrast microscopy image sequences (C3H10T1/2& C2C12) show that the proposed method can leverage the datasets from multiple domains to boost the performance by effectively transferring the knowledge from the auxiliary domain to the target domain. Therefore, it can overcome the inconsistence of feature distributions in different domains.