| 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 | 95 | 2018 |
| 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, ... Neurocomputing 365, 31-43, 2019 | 44 | 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 | 38 | 2019 |
| 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 | 30 | 2019 |
| 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 | 30 | 2019 |
| 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 | 25 | 2019 |
| 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 | 7 | 2019 |
| 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 | 5 | 2020 |
| 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 | 3 | 2018 |
| Learning More with Less: GAN-based Medical Image Augmentation C Han, K Murao, H NAKAYAMA Medical Imaging Technology 37 (3), 137-142, 2019 | 2* | 2019 |
| MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction C Han, L Rundo, K Murao, T Noguchi, Y Shimahara, ZA Milacski, ... arXiv preprint arXiv:2007.13559, 2020 | 1 | 2020 |
| 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 | 1 | 2019 |
| Prostate Zonal Segmentation Using Deep Learning C Han, J Zhang, R Hataya, Y Nagano, H Nakayama, L Rundo Symposium on Medical Imaging (MI), 2018 | 1 | 2018 |
| 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 |
| 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 |
| 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, ... | | |
Hideki NakayamaThe University of Tokyo, Associate ProfessorEmail verificata su ci.i.u-tokyo.ac.jp
