A key step for mitigating tumor recurrence for patients with head and neck cancer is adequate surgical margin delineation. Presently available techniques however limit accurate tumor margin detection during surgery. Herein, we report on tumor visualization using deep learning by combining autofluorescence images acquired by a fiber-based fluorescence lifetime imaging (FLIm) system and white light images (WLI) obtained by surgical cameras. To accomplish accurate registration between FLIm and WLI, a tissue motion correction algorithm was employed as a pre-processing step. The trained model was applied to differentiation of healthy and cancerous tissues in a 50 head and neck cancer patients dataset (ROC-AUC : 0.87).
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