Cell Encoding for Histopathology Image Classification

Although many image analysis algorithms can achieve good performance with sufficient number of labeled images, manually labeling images by pathologists is time consuming and expensive. Meanwhile, with the development of cell detection and segmentation techniques, it is possible to classify pathology images by using cell-level information, which is crucial to grade different diseases; however, it is still very challenging to efficiently conduct cell analysis on large-scale image databases since one image often contains a large number of cells. To address these issues, in this paper, we present a novel cell-based framework that requires only a few labeled images to classify large-scale pathology ones. Specifically, we encode each cell into a set of binary codes to generate image representation using a semi-supervised hashing model, which can take advantage of both labeled and unlabeled cells. Thereafter, we map all the binary codes in one whole image into a single histogram vector and then learn a support vector machine for image classification. The proposed framework is validated on one large-scale lung cancer image dataset with two types of diseases, and it can achieve 87.88% classification accuracy on 800 test images using only 5 labeled images of each disease.