Changhee Han
Changhee Han
Ph.D. in Computer Science, LPIXEL Inc.
確認したメール アドレス: lpixel.net - ホームページ
タイトル
引用先
引用先
GAN-based synthetic brain MR image generation
C Han, H Hayashi, L Rundo, R Araki, W Shimoda, S Muramatsu, ...
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018
552018
Learning More with Less: GAN-based Medical Image Augmentation
C Han, K Murao, S Satoh, H Nakayama
Medical Imaging Technology 37 (3), 137-142, 2019
24*2019
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images
C Han, K Murao, T Noguchi, Y Kawata, F Uchiyama, L Rundo, ...
ACM International Conference on Information and Knowledge Management (CIKM), 2019
242019
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
L Rundo, C Han, Y Nagano, J Zhang, R Hataya, C Militello, A Tangherloni, ...
Neurocomputing 365, 31-43, 2019
222019
Combining noise-to-image and image-to-image GANs: Brain MR image augmentation for tumor detection
C Han, L Rundo, R Araki, Y Nagano, Y Furukawa, G Mauri, H Nakayama, ...
IEEE Access, 2019
142019
Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection
C Han, L Rundo, R Araki, Y Furukawa, G Mauri, H Nakayama, H Hayashi
Neural Approaches to Dynamics of Signal Exchanges, 2019
132019
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection
C Han, Y Kitamura, A Kudo, A Ichinose, L Rundo, Y Furukawa, ...
International Conference on 3D Vision (3DV), 2019
122019
CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
L Rundo, C Han, J Zhang, R Hataya, Y Nagano, C Militello, C Ferretti, ...
Neural Approaches to Dynamics of Signal Exchanges, 2019
52019
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems
C Han, L Rundo, K Murao, T Nemoto, H Nakayama
IFIP International Conference on Artificial Intelligence Applications and …, 2020
22020
Infinite Brain Tumor Images: Can GAN-based Data Augmentation Improve Tumor Detection on MR Images?
C Han, L Rundo, R Araki, Y Furukawa, G Mauri, H Nakayama, H Hayashi
The 21st Meeting on Image Recognition and Understanding (MIRU), 2018
22018
Cloud platform for deep learning-based CAD via collaboration between Japanese medical societies and institutes of informatics
K Murao, Y Ninomiya, C Han, K Aida, S Satoh
Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and …, 2020
2020
CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images
L Rundo, C Han, J Zhang, R Hataya, Y Nagano, C Militello, C Ferretti, ...
Neural Approaches to Dynamics of Signal Exchanges 151, 269, 2019
2019
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis
C Han, L Rundo, K Murao, ZÁ Milacski, K Umemoto, H Nakayama, ...
Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB), 2019
2019
CNN-based Prostate Zonal Segmentation on Magnetic Resonance Images
C Han, L Rundo, J Zhang, R Hataya, Y Nagano, G Mauri, H Nakayama
The 21st Meeting on Image Recognition and Understanding (MIRU), 2018
2018
Prostate Zonal Segmentation Using Deep Learning
C Han, J Zhang, R Hataya, Y Nagano, H Nakayama, L Rundo
Symposium on Medical Imaging (MI), 2018
2018
Application of Learning Classifier Systems to Gene Expression Analysis in Synthetic Biology
C Han, K Tsuge, H Iba
Nature-Inspired Computing and Optimization, 247-275, 2017
2017
Optimization of artificial operon construction by consultation algorithms utilizing LCS
C Han, K Tsuge, H Iba
2016 IEEE Congress on Evolutionary Computation (CEC), 4273-4280, 2016
2016
CNN-based MRI Regression Using U-Net
C HAN, F GESSER, ZA MILACSKI
2016
TOWARDS ANNOTATING LESS MEDICAL IMAGES: PGGAN-BASED MR IMAGE AUGMENTATION FOR BRAIN TUMOR DETECTION
C Han, H Hayashi, L Rundo, R Araki, Y Nagano, Y Furukawa, G Mauri, ...
現在システムで処理を実行できません。しばらくしてからもう一度お試しください。
論文 1–19