Decision Boundary Re-Sampling in Imbalanced Learning for Ulcer Detection

• Main Authors: Changhoo Lee, Dongwook Shin, Junki Min, Jaemyung Cha, Seungkyu Lee
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Decision boundary re-sampling; convolutional neural network; ulcer classification
• Online Access: https://ieeexplore.ieee.org/document/9216051/
• ISSN: 2169-3536

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.