Shoji Sonoyama

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In this paper we report results for recognizing colorectal NBI endoscopic images by using features extracted from convolutional neural network (CNN). In this comparative study, we extract features from different layers from different CNN models, and then train linear SVM classifiers. Experimental results with 10-fold cross validations show that features(More)
We address a problem of endoscopic image classification taken by different (e.g., old and new) endoscopies. Our proposed method formulates the problem as a constraint optimization that estimates a linear transformation between feature vectors (or Bag-of-Visual words histograms) in a framework of transfer learning. Experimental results show that the proposed(More)
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(More)
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(More)
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