Vascular pattern recognition for narrow-band imaging bronchoscopy

Narrow-band imaging (NBI) bronchoscopy offers enhanced visualization of microvascular structures in the lung's epithelium (airway walls). Recent studies suggest that such vessels are helpful in predicting the invasiveness of bronchial lesions. In particular, Shibuya characterized pathological features of lesions and studied their relationship with specific histological stages of lung cancer. We propose a method for identifying these vascular patterns using a small expert-labeled dataset. Our approach is based on a few-shot learning method using a Siamese network to learn and distinguish pathological features of the bronchial vasculature. We achieved better intra-class clustering and inter-class separation in the embedding space compared to a baseline CNN classifier. Further, a 25% increase in the overall accuracy was obtained during testing.