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1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Kamada, Shin Ichimura, Takumi Harada, Toshihide Hiroshima City Univ Grad Sch Informat Sci Asaminami Ku 3-4-1 Ozuka Higashi Hiroshima 7313194 Japan Prefectural Univ Hiroshima Fac Management & Informat Syst Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348559 Japan Prefectural Univ Hiroshima Fac Hlth & Welf Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348559 Japan

ISBN: (纸本)9781538695555

Deep learning has a hierarchical network architecture to represent the complicated feature of input patterns. The adaptive stnictural learning method of Deep Belief Network (DBN) has been developed. The method can discover au optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm, and generate a new hidden layer in DBN by the extension of the algorithm. In this paper, the proposed adaptive structural learning of DBN was applied to the comprehensive medical examination data for the cancer prediction. The prediction system shows higher classification accuracy (99.8% for training and 95.5% for test) than the traditional DBN. Moreover, the explicit knowledge with respect to the relation between input and output patterns was extracted from the trained DBN network by C4.5. Some characteristics extracted in the form of IF-THEN rules to find an initial cancer at the early stage were reported in this paper.

关键词： Deep learning RBM DBN adaptive structural learning Knowledge Extraction C4.5

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An adaptive structural learning of Deep Belief Network for Image-based Crack Detection in Concrete Structures Using SDNET2018 1

An Adaptive Structural Learning of Deep Belief Network for I...

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1st International Conference on Image Processing and Robotics (ICIP)

作者： Kamada, Shin Ichimura, Takumi Iwasaki, Takashi Prefectural Univ Hiroshima Res Org Reg Oriented Studies Adv Artificial Intelligence Project Res Ctr Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348558 Japan Prefectural Univ Hiroshima Res Org Reg Oriented Studies Adv Artificial Intelligence Project Res Ctr Fac Management & Informat SystMinami Ku 1-1-71 Ujina Higashi Hiroshima 7348558 Japan Mitsui Consultants Co Ltd MCC Lab Infrastruct Syst Grp Shinagawa Ku Gate City Ohsaki West Tower 15F1-11-1 Osaki Tokyo 1410032 Japan

ISBN: (纸本)9781728165417

We have developed an adaptive structural Deep Belief Network (adaptive DBN) that finds an optimal network structure in a self-organizing manner during learning. The adaptive DBN is the hierarchical architecture where each layer employs adaptive Restricted Boltzmann Machine (adaptive RBM). The adaptive RBM can find the appropriate number of hidden neurons during learning. The proposed method was applied to a concrete image benchmark data set SDNET2018 for crack detection. The dataset contains about 56,000 crack images for three types of concrete structures: bridge decks, walls, and paved roads. The fine-tuning method of the adaptive DBN can show 99.7%, 99.7%, and 99.4% classification accuracy for three types of structures. However, we found the database included some wrong annotated data which cannot be judged from images by human experts. This paper discusses consideration that purses the major factor for the wrong cases and the removal of the adversarial examples from the dataset.

关键词： adaptive structural learning Deep Belief Network Deep learning Crack Detection SDNET2018 Adversarial Data

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Knowledge Extraction of adaptive structural learning of Deep Belief Network for Medical Examination Data

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INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2019年第1期13卷 67-86页

作者： Kamada, Shin Ichimura, Takumi Harada, Toshihide Hiroshima City Univ Grad Sch Informat Sci Asa Minami Ku 3-4-1 Ozuka Higashi Hiroshima 7313194 Japan Prefectural Univ Hiroshima Fac Management & Informat Syst Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348559 Japan Prefectural Univ Hiroshima Fac Hlth & Welf Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348559 Japan

Deep learning has a hierarchical network structure to represent multiple features of input data. The adaptive structural learning method of Deep Belief Network (DBN) can reach the high classification capability while searching the optimal network structure during the training. The method can find the optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm, and generate a new hidden layer in DBN by the extension of the algorithm. In this paper, the proposed adaptive structural learning of DBN (adaptive DBN) was applied to the comprehensive medical examination data for cancer prediction. The developed prediction system showed higher classification accuracy for test data (99.5% for the lung cancer and 94.3% for the stomach cancer) than the several learning methods such as traditional RBM, DBN, Non-Linear Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Moreover, the explicit knowledge that makes the inference process of the trained DBN is required in deep learning. The binary patterns of activated neurons for given input in RBM and the hierarchical structure of DBN can represent the relation between input and output signals. These binary patterns were classified by C4.5 for knowledge extraction. Although the extracted knowledge showed slightly lower classification accuracy than the trained DBN network, it was able to improve inference speed by about 1/40. We report that the extracted IF-THEN rules from the trained DBN for medical examination data showed some interesting features related to initial condition of cancer.

关键词： Deep learning RBM DBN adaptive structural learning knowledge extraction

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Re-learning of Child Model for Misclassified data by using KL Divergence in AffectNet: A Database for Facial Expression 11

Re-learning of Child Model for Misclassified data by using K...

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IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)

作者： Ichimura, Takumi Kamada, Shin Prefectural Univ Hiroshima Res Org Reg Oriented Studies Adv Artificial Intelligence Project Res Ctr Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348558 Japan Prefectural Univ Hiroshima Fac Management & Informat Syst Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348558 Japan

ISBN: (纸本)9781728124292

AffectNet contains more than 1,000,000 facial images which manually annotated for the presence of eight discrete facial expressions and the intensity of valence and arousal. adaptive structural learning method of DBN (adaptive DBN) is positioned as a top Deep learning model of classification capability for some large image benchmark databases. The Convolutional Neural Network and adaptive DBN were trained for AffectNet and classification capability was compared. adaptive DBN showed higher classification ratio. However, the model was not able to classify some test cases correctly because human emotions contain many ambiguous features or patterns leading wrong answer which includes the possibility of being a factor of adversarial examples, due to two or more annotators answer different subjective judgment for an image. In order to distinguish such cases, this paper investigated a re-learning model of adaptive DBN with two or more child models, where the original trained model can be seen as a parent model and then new child models are generated for some misclassified cases. In addition, an appropriate child model was generated according to difference between two models by using KL divergence. The generated child models showed better performance to classify two emotion categories: 'Disgust' and 'Anger'.

关键词： adaptive structural learning Deep Belief Network Restricted Boltzmann Machine Kullback-Leibler Divergence Child model Facial Expression AffectNet

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A Segmentation Method of Lung Tumor by using adaptive structural Deep Belief Network 62

A Segmentation Method of Lung Tumor by using Adaptive Struct...

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62nd Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE)

作者： Kamada, Shin Ichimura, Takumi Hiroshima City Univ Grad Sch Informat Sci Hiroshima Japan Prefectural Univ Hiroshima Fac Reg Dev Hiroshima Japan

ISBN: (纸本)9784907764807

Deep learning has a hierarchical network architecture to represent the complicated feature of input patterns. In the previous research, the adaptive structure learning method of Deep Belief Network (adaptive DBN) was developed, which can discover an optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm, and can obtain appropriate number of hidden layers in DBN. In this paper, a new segmentation method using the adaptive DBN was developed to extract lung tumor from 3D CT images. In the experiment, 156 cases collected by an open dataset in NSCLC Radiogenomics was used to evaluate our model. As a result, our model showed 0.884 dice coefficient for the test data, which was higher value than the 3D U-Net implemented on MONAI.

关键词： Deep learning RBM DBN adaptive structural learning Segmentation Lung Tumor in NSCLC Radiogenomics

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