Decision Boundary Re-Sampling in Imbalanced Learning for Ulcer Detection

Data imbalance problem between normal and lesion endoscopy images makes it difficult to employ deep learning approaches in automatic Ulcer detection and classification. Due to the large variety of normal images in their appearance, characterizing ulcer with limited training samples is not a trivial task. In this work, we propose decision boundary re-sampling (DBR) in imbalanced learning that extrapolates ulcer samples in a latent space of deep convolutional neural network. Proposed method shows improved ulcer classification performance on wireless endoscopy images compared to state-of-the-art methods.