Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification

Yang Song, Qing Li, Heng Huang, Dagan Feng, Mei Chen, Weidong Cai
2017 IEEE Transactions on Medical Imaging  
Microscopy image classification is important in various biomedical applications, such as cancer subtype identification and protein localization for high content screening. To achieve automated and effective microscopy image classification, the representative and discriminative capability of image feature descriptors is essential. To this end, in this study we propose a new feature representation algorithm to facilitate automated microscopy image classification. In particular, we incorporate
more » ... er vector (FV) encoding with multiple types of local features that are handcrafted or learned, and we design a separationguided dimension reduction (SDR) method to reduce the descriptor dimension while increasing its discriminative capability. Our method is evaluated on four publicly available microscopy image datasets of different imaging types and applications, including the UCSB breast cancer dataset, MICCAI 2015 CBTC challenge dataset, and IICBU malignant lymphoma and RNAi datasets. Our experimental results demonstrate the advantage of the proposed low-dimensional FV representation, showing consistent performance improvement over the existing state-of-the-art and the commonly used dimension reduction techniques.
doi:10.1109/tmi.2017.2687466 pmid:28358678 fatcat:rq76wyzscbgl3j3ajpjvqrsnmu