Hybrid deep autoencoder with Curvature Gaussian for detection of various types of cells in bone marrow trephine biopsy images
Automated cell detection is a critical step for a number of computer-assisted pathology related image analysis algorithm. However, automated cell detection is complicated due to the variable cytomorphological and histological factors associated with each cell. In order to efficiently resolve the challenge of automated cell detection, deep learning strategies are widely applied and have recently been shown to be successful in histopathological images. In this paper, we concentrate on bone marrow trephine biopsy images and propose a hybrid deep autoencoder (HDA) network with Curvature Gaussian model for efficient and precise bone marrow hematopoietic stem cell detection via related high-level feature correspondence. The accuracy of our proposed method is up to 94%, outperforming other supervised and unsupervised detection approaches.