  We apply deep learning (DL) on Magnetic resonance spectroscopy (MRS) data for
the task of brain tumor detection. Medical applications often suffer from data
scarcity and corruption by noise. Both of these problems are prominent in our
data set. Furthermore, a varying number of spectra are available for the
different patients. We address these issues by considering the task as a
multiple instance learning (MIL) problem. Specifically, we aggregate multiple
spectra from the same patient into a "bag" for classification and apply data
augmentation techniques. To achieve the permutation invariance during the
process of bagging, we proposed two approaches: (1) to apply min-, max-, and
average-pooling on the features of all samples in one bag and (2) to apply an
attention mechanism. We tested these two approaches on multiple neural network
architectures. We demonstrate that classification performance is significantly
improved when training on multiple instances rather than single spectra. We
propose a simple oversampling data augmentation method and show that it could
further improve the performance. Finally, we demonstrate that our proposed
model outperforms manual classification by neuroradiologists according to most
performance metrics.
