An Effective Multi-classification Method for NHL Pathological Images

Accurate classification on pathological images is a significant research focus such as for non-Hodgkin lymphomas (NHL). To this end, this paper proposes a hierarchical classification model based on the labels' statistics for three NHL pathological images, including chronic lymphocytic leukemia (CLL), follicular lymphoma (FL) and mantle cell lymphoma (MCL). First, each pathological image is converted onto the grayscale channel and then divided into 130 non-overlapped patches with 100100 pixels. Next, the sparse autoencoder (SAE), an unsupervised feature extraction method, is utilized to learn the representations of all patches and meanwhile texture features are extracted on these patches which are considered as the hand-craft features. Following this process, we can obtain a 680-dimension feature set. Finally, a hierarchical classification model trained by these 680-dimension features is applied to classify NHL as CLL, FL and MCL, where the label of each NHL pathological image is determined via the output labels of its 130 patches. The experimental results and comparisons demonstrate the advantages of the proposed hierarchical classification model.