Deep Margin Cosine Autoencoder-Based Medical Hyperspectral Image Classification for Tumor Diagnosis

Deep learning has become a powerful tool to automatically classify medical hyperspectral images (MedHSIs) for the diagnosis of various tumors such as cancer. These deep learning based classification approaches consist of both feature extraction and disease prediction, which are independent of each other. Therefore, the extracted features may be incompatible with the used classifier for prediction. To remedy such deficiency, in this work, we propose a novel deep method, i.e ., deep margin cosine autoencoder (DMCA), for the MedHSI classification, which provides solid support to diagnose tumors. To be specific, first , we consider deep autoencoder (DAE) network as the basic framework for the feature extraction of the MedHSI, and the soft-max classifier is introduced as the output layer to predict the results. Second , to report the features well compatible with the classifier, the importance of the soft-max is added as a constraint into the DAE network. Further, a cosine margin is introduced to enhance the discrimination of different feature clusters. In addition, we also design an optimization scheme to the solutions of the DMCA model. Third , a two-stage training strategy is presented to train the built DMCA network. After completing the training, unknown tissues ( i.e ., unlabeled samples) can be specified to determine whether they are tumors or not. Sufficient experimental results have been provided to validate that our DMCA method achieves better classification performance and higher tumor diagnosis accuracy compared with some advanced approaches.