Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection

• URL: http://arxiv.org/abs/2004.10159v1
• Date: Tue, 21 Apr 2020 17:07:18 GMT
• Title: Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection
• Authors: Marcel Bengs and Stephan Westermann and Nils Gessert and Dennis Eggert and Andreas O. H. Gerstner and Nina A. Mueller and Christian Betz and Wiebke Laffers and Alexander Schlaefer
• Abstract summary: Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
• Score: 49.32653090178743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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