Kanghan Oh
Chonbuk National University
12 Papers
19 Citations
Kanghan Oh is an academic researcher from Chonbuk National University. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 5, co-authored 9 publications.
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Papers
Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning
TL;DR: This study presents an approach meant to encourage the end-to-end learning of a volumetric convolutional neural network (CNN) model for four binary classification tasks based on magnetic resonance imaging (MRI) and visualizes its outcomes in terms of the decision of the CNNs without any human intervention.
Classification of schizophrenia and normal controls using 3D convolutional neural network and outcome visualization.
TL;DR: The findings suggest that the proposed 3D-CAE-based CNN can classify patients with SSDs and controls with higher accuracy compared to other models, and Visualization of salient regions provides important clinical information.
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Regional Multi-Scale Approach for Visually Pleasing Explanations of Deep Neural Networks
Dasom Seo,Kanghan Oh,Il-Seok Oh +2 more
TL;DR: In this paper, a region-based approach that estimates feature importance in terms of appropriately segmented regions is proposed, by fusing the saliency maps generated from multi-scale segmentations, a more class discriminative and visually pleasing map is obtained.
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Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach
Kanghan Oh,Il-Seok Oh,Uyanga Tsogt,Jie Shen,Woo-Sung Kim,Congcong Liu,Nam-In Kang,Keon-Hak Lee,Jing Sui,Sung-Wan Kim,Young Chul Chung +10 more
TL;DR: In this paper , the authors developed the BrainNet-Global Covariance Pooling-Attention Convolutional Neural Network (BrainNet-GA CNN) by incorporating BrainNetCNN and global covariance pooling into the self-attention mechanism.
Diagnosis of Schizophrenia with Functional Connectome Data: A Graph-Based Convolutional Neural Network Approach
Kanghan Oh,Il-Seok Oh,Uyanga Tsogt,Jie Shen,Woo-Sung Kim,Congcong Liu,Nam-In Kang,Keon-Hak Lee,Jing Sui,Sung-Wan Kim,Young-Chul Chung +10 more
TL;DR: The findings suggest that the proposed BrainGCPNet can classify patients with SSDs and HCs with higher accuracy than other models and highlight the potential use of the BrainG CPNet in the diagnosis of schizophrenia.