  Early detection of head and neck tumors is crucial for patient survival.
Often, diagnoses are made based on endoscopic examination of the larynx
followed by biopsy and histological analysis, leading to a high inter-observer
variability due to subjective assessment. In this regard, early non-invasive
diagnostics independent of the clinician would be a valuable tool. A recent
study has shown that hyperspectral imaging (HSI) can be used for non-invasive
detection of head and neck tumors, as precancerous or cancerous lesions show
specific spectral signatures that distinguish them from healthy tissue.
However, HSI data processing is challenging due to high spectral variations,
various image interferences, and the high dimensionality of the data.
Therefore, performance of automatic HSI analysis has been limited and so far,
mostly ex-vivo studies have been presented with deep learning. In this work, we
analyze deep learning techniques for in-vivo hyperspectral laryngeal cancer
detection. For this purpose we design and evaluate convolutional neural
networks (CNNs) with 2D spatial or 3D spatio-spectral convolutions combined
with a state-of-the-art Densenet architecture. For evaluation, we use an
in-vivo data set with HSI of the oral cavity or oropharynx. Overall, we present
multiple deep learning techniques for in-vivo laryngeal cancer detection based
on HSI and we show that jointly learning from the spatial and spectral domain
improves classification accuracy notably. Our 3D spatio-spectral Densenet
achieves an average accuracy of 81%.
