Transfer Learning for Endoscopic Image Classification
This is an incremental improvement for medical imaging, specifically endoscopic image classification, addressing domain shift between old and new endoscopes.
The paper tackled the problem of transferring features between endoscopic images from different endoscopes by extending Max-Margin Domain Transfer with L2 distance constraints and a dual formulation to reduce computation cost, resulting in MMDTL2 outperforming MMDT on real datasets.
In this paper we propose a method for transfer learning of endoscopic images. For transferring between features obtained from images taken by different (old and new) endoscopes, we extend the Max-Margin Domain Transfer (MMDT) proposed by Hoffman et al. in order to use L2 distance constraints as regularization, called Max-Margin Domain Transfer with L2 Distance Constraints (MMDTL2). Furthermore, we develop the dual formulation of the optimization problem in order to reduce the computation cost. Experimental results demonstrate that the proposed MMDTL2 outperforms MMDT for real data sets taken by different endoscopes.