- T. Nemoto, A. Takeda, Y. Matsuo, N. Kishi, T. Eriguchi, E. Kunieda, R. Kimura, N. Sanuki, Y. Tsurugai, M. Yagi, Y. Aoki, Y. Oku, Y. Kimura, C. Han, N. Shigematsu, Applying Artificial Neural Networks to Develop a Decision Support Tool for Tis–4N0M0 Non–Small-Cell Lung Cancer Treated With Stereotactic Body Radiotherapy, JCO clinical cancer informatics (impact factor: ~4.5), June 2022.
- S. Nakazawa, C. Han, J. Hasei, R. Nakahara, T. Ozaki, BAPGAN: GAN-based Bone Age Progression of Femur and Phalange X-ray Images, In SPIE Medical Imaging, San Diego, The United States, February 2022.
- L. Rundo, C. Militello, V. Conti, F. Zaccagna, C. Han, Advanced Computational Methods for Oncological Image Analysis, Journal of Imaging (impact factor: 3.8), November 2021. *editorial
- E. C. de Farias, C. Di Noia, C. Han, E. Sala, M. Castelli, L. Rundo, Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features, Scientific Reports (impact factor: 4.4), November 2021.
- C. Han, L. Rundo, K. Murao, T. Noguchi, Y. Shimahara, Z. Á. Milacski, S. Koshino, E. Sala, H. Nakayama, S. Satoh, MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction, BMC Bioinformatics (impact factor: 3.2), April 2021.
- C. Han, T. Okamoto, K. Takeuchi, D. Katsios, A. Grushnikov, M. Kobayashi, A. Choppin, Y. Kurashina, Y. Shimahara, Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis: A Survey, Medical Imaging and Information Sciences, Japan Society of Medical Imaging and Information Sciences, April 2021.
- C. Han, L. Rundo, K. Murao, T. Nemoto, H. Nakayama, Bridging the Gap between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems, In International Conference on Artificial Intelligence Applications and Innovations (AIAI), Halkidiki, Greece, June 2020.
- K. Murao, Y. Ninomiya, C. Han, K. Aida, S. Satoh, Cloud platform for deep learning-based CAD via collaboration between Japanese medical societies and institutes of informatics, In SPIE Medical Imaging, Houston, The United States, February 2020.
- C. Han, K. Murao, T. Noguchi, Y. Kawata, F. Uchiyama, L. Rundo, H. Nakayama, S. Satoh, Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images, In ACM International Conference on Information and Knowledge Management (CIKM, acceptance rate: ~19%), Beijing, China, November 2019.
- C. Han, L. Rundo, R. Araki, Y. Nagano, Y. Furukawa, G. Mauri, H. Nakayama, H. Hayashi, Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection, IEEE Access (impact factor: 4.1), October 2019.
- C. Han, L. Rundo, R. Araki, Y. Furukawa, G. Mauri, H. Nakayama, H. Hayashi, Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection, In A. Esposito, M. Faundez-Zanuy, F. C. Morabito, E. Pasero (eds.) Neural Approaches to Dynamics of Signal Exchanges, Springer, September 2019.
- L. Rundo, C. Han, J. Zhang, R. Hataya, Y. Nagano, C. Militello, C. Ferretti, M.S. Nobile, A. Tangherloni, M.C. Gilardi, S. Vitabile, H. Nakayama, G. Mauri, CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study, In A. Esposito, M. Faundez-Zanuy, F. C. Morabito, E. Pasero (eds.) Neural Approaches to Dynamics of Signal Exchanges, Springer, September 2019.
- C. Han, Y. Kitamura, A. Kudo, A. Ichinose, L. Rundo, Y. Furukawa, K. Umemoto, H. Nakayama, Y. Li, Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection, In International Conference on 3D Vision (3DV), Québec City, Canada, September 2019.
- C. Han, L. Rundo, K. Murao, Z. Á. Milacski, K. Umemoto, H. Nakayama, S. Satoh, GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer’s Disease Diagnosis, In Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB), Bergamo, Italy, September 2019.
- C. Han*, L. Rundo*, Y. Nagano, J. Zhang, R. Hataya, C. Militello, A. Tangherloni, M. S. Nobile, C. Ferretti, D. Besozzi, M. C. Gilardi, S. Vitabile, G. Mauri, H. Nakayama, P. Cazzaniga, USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets, Neurocomputing (impact factor: 4.1), July 2019. * denotes Co-first Authors
- C. Han, K. Murao, S. Satoh, H. Nakayama, Learning More with Less: GAN-based Medical Image Augmentation, Medical Imaging Technology, Japanese Society of Medical Imaging Technology, June 2019.
- C. Han, H. Hayashi, L. Rundo, R. Araki, Y. Furukawa, W. Shimoda, S. Muramatsu, G. Mauri, H. Nakayama, GAN-based Synthetic Brain MR Image Generation, In IEEE International Symposium on Biomedical Imaging (ISBI), Washington, D.C., The United States, April 2018.
- A. Fukuda, C. Han, K. Hakamada, Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans, In IEEE International Conference on Image Processing (ICIP), Anchorage, The United States, August 2021.
- C. Han, K. Tsuge, H. Iba, Application of Learning Classifier Systems to Gene Expression Analysis in Synthetic Biology, In S. Patnaik, X. Yang, and K. Nakamatsu (eds.) Nature Inspired Computing and Optimization: Theory and Applications, Springer, March 2017.
- C. Han, K. Tsuge, H. Iba, Optimization of Artificial Operon Construction by Consultation Algorithms Utilizing LCS, In IEEE Congress on Evolutionary Computation (CEC), Vancouver, Canada, July 2016.