Domain Adaptation with L2 constraints for classifying images from different endoscope systems
This work addresses the problem of domain shift in medical imaging for clinicians, but it is incremental as it builds on an existing method with minor modifications.
The paper tackles domain adaptation for classifying images from different narrow band imaging (NBI) endoscope systems by extending maximum margin domain transfer with L2 constraints, resulting in improved performance over baseline methods, especially with high-dimensional features and over 20 training samples per class.
This paper proposes a method for domain adaptation that extends the maximum margin domain transfer (MMDT) proposed by Hoffman et al., by introducing L2 distance constraints between samples of different domains; thus, our method is denoted as MMDTL2. Motivated by the differences between the images taken by narrow band imaging (NBI) endoscopic devices, we utilize different NBI devices as different domains and estimate the transformations between samples of different domains, i.e., image samples taken by different NBI endoscope systems. We first formulate the problem in the primal form, and then derive the dual form with much lesser computational costs as compared to the naive approach. From our experimental results using NBI image datasets from two different NBI endoscopic devices, we find that MMDTL2 is better than MMDT and also support vector machines without adaptation, especially when NBI image features are high-dimensional and the per-class training samples are greater than 20.