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Narrow Band Imaging (NBI) is increasingly being used in laryngology because it increases the visibility of mucosal vascular patterns which serve as important visual markers to detect premalignant, dysplastic, and malignant lesions. To this end, deep learning methods have been used to automatically detect and classify the lesions from NBI endoscopic videos. However, the heterogeneity of the lesions, illumination changes due to phlegm on the mucosa, and imaging artifacts such as blurriness make inter-patient endoscopic videos exhibit diverging image distributions. Therefore, learning representations that are robust to image distribution changes can be beneficial and improve the generalizing capability of the convolutional neural network (CNN). To this end, we propose a dual branch CNN that learns robust representations by combining deep narrow band features and wavelet scattering transform features of the narrow band images to classify vocal cord NBI images into malignant and benign classes. We show the generalizing capability of our learnt representation by training our neural network using two different losses: cross-entropy (CE) loss and supervised contrastive (SupCon) loss.
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Wednesday 6th July
Poster Session 1.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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