9 citations · 16 across the 6 of their papers we have counts for
6 papers
Learning from AI: An Interactive Learning Method Using a DNN Model Incorporating Expert Knowledge as a Teacher
Kohei Hattori, Tsubasa Hirakawa, Takayoshi Yamashita +1
Visual explanation is an approach for visualizing the grounds of judgment by deep learning, and it is possible to visually interpret the grounds of a judgment for a certain input b…
Masking and Mixing Adversarial Training
Hiroki Adachi, Tsubasa Hirakawa, Takayoshi Yamashita +3
While convolutional neural networks (CNNs) have achieved excellent performances in various computer vision tasks, they often misclassify with malicious samples, a.k.a. adversarial…
Visual Explanation of Deep Q-Network for Robot Navigation by Fine-tuning Attention Branch
Yuya Maruyama, Hiroshi Fukui, Tsubasa Hirakawa +3
Robot navigation with deep reinforcement learning (RL) achieves higher performance and performs well under complex environment. Meanwhile, the interpretation of the decision-making…
Development of a Real-time Colorectal Tumor Classification System for Narrow-band Imaging zoom-videoendoscopy
Tsubasa Hirakawa, Toru Tamaki, Bisser Raytchev +5
Colorectal endoscopy is important for the early detection and treatment of colorectal cancer and is used worldwide. A computer-aided diagnosis (CAD) system that provides an objecti…
Transfer Learning for Endoscopic Image Classification
Shoji Sonoyama, Toru Tamaki, Tsubasa Hirakawa +6
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…
Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using CNN Features
Toru Tamaki, Shoji Sonoyama, Tsubasa Hirakawa +6
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…