Weakly-Supervised Brain Tumor Classification with Global Diagnosis Label

There is an increasing need for efficient and automatic evaluation of brain tumors on magnetic resonance images (MRI). Most of the previous works focus on segmentation, registration, and growth modeling of the most common primary brain tumor gliomas, or the classification of up to three types of brain tumors. In this work, we extend the study to eight types of brain tumors where only global diagnosis labels are given but not the slice-level labels. We propose a weakly supervised method and demonstrate that inferring disease types at the slice-level would help the global label prediction. We also provide an algorithm for feature extraction via randomly choosing connection paths through class-specific autoencoders with dropout to accommodate the small-dataset problem. Experimental results on both public and proprietary datasets are compared to the baseline methods. The classification with the weakly supervised setting on the proprietary data, consisting of 295 patients with eight different tumor types, shows close results to the upper bound in the supervised learning setting.