  Early detection of cancerous tissue is crucial for long-term patient
survival. In the head and neck region, a typical diagnostic procedure is an
endoscopic intervention where a medical expert manually assesses tissue using
RGB camera images. While healthy and tumor regions are generally easier to
distinguish, differentiating benign and malignant tumors is very challenging.
This requires an invasive biopsy, followed by histological evaluation for
diagnosis. Also, during tumor resection, tumor margins need to be verified by
histological analysis. To avoid unnecessary tissue resection, a non-invasive,
image-based diagnostic tool would be very valuable. Recently, hyperspectral
imaging paired with deep learning has been proposed for this task,
demonstrating promising results on ex-vivo specimens. In this work, we
demonstrate the feasibility of in-vivo tumor type classification using
hyperspectral imaging and deep learning. We analyze the value of using multiple
hyperspectral bands compared to conventional RGB images and we study several
machine learning models' ability to make use of the additional spectral
information. Based on our insights, we address spectral and spatial processing
using recurrent-convolutional models for effective spectral aggregating and
spatial feature learning. Our best model achieves an AUC of 76.3%,
significantly outperforming previous conventional and deep learning methods.
