1. 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.
  2. 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.
  3. L. Rundo, C. Militello, V. Conti, F. Zaccagna, C. HanAdvanced Computational Methods for Oncological Image Analysis, Journal of Imaging (impact factor: 3.8), November 2021.  *editorial
  4. E. C. de Farias, C. Di Noia, C. Han, E. Sala, M. Castelli, L. RundoImpact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features, Scientific Reports (impact factor: 4.4), November 2021.
  5. C. Han, L. Rundo, K. Murao, T. Noguchi, Y. Shimahara, Z. Á. Milacski, S. Koshino, E. Sala, H. Nakayama, S. SatohMADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction, BMC Bioinformatics (impact factor: 3.2), April 2021.
  6. C. Han, T. Okamoto, K. Takeuchi, D. Katsios, A. Grushnikov, M. Kobayashi, A. Choppin, Y. Kurashina, Y. ShimaharaTips 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.
  7. C. Han, L. Rundo, K. Murao, T. Nemoto, H. NakayamaBridging 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.
  8. K. Murao, Y. Ninomiya, C. Han, K. Aida, S. SatohCloud 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.
  9. C. Han, K. Murao, T. Noguchi, Y. Kawata, F. Uchiyama, L. Rundo, H. Nakayama, S. SatohLearning 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.
  10. C. Han, L. Rundo, R. Araki, Y. Nagano, Y. Furukawa, G. Mauri, H. Nakayama, H. HayashiCombining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection, IEEE Access (impact factor: 4.1), October 2019.
  11. C. Han, L. Rundo, R. Araki, Y. Furukawa, G. Mauri, H. Nakayama, H. HayashiInfinite 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. 
  12. 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.  
  13. C. Han, Y. Kitamura, A. Kudo, A. Ichinose, L. Rundo, Y. Furukawa, K. Umemoto, H. Nakayama, Y. LiSynthesizing 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.
  14. C. Han, L. Rundo, K. Murao, Z. Á. Milacski, K. Umemoto, H. Nakayama, S. SatohGAN-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.
  15. 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
  16. C. Han, K. Murao, S. Satoh, H. NakayamaLearning More with Less: GAN-based Medical Image Augmentation, Medical Imaging Technology, Japanese Society of Medical Imaging Technology, June 2019.
  17. C. Han, H. Hayashi, L. Rundo, R. Araki, Y. Furukawa, W. Shimoda, S. Muramatsu, G. Mauri, H. NakayamaGAN-based Synthetic Brain MR Image Generation, In IEEE International Symposium on Biomedical Imaging (ISBI), Washington, D.C., The United States, April 2018.
  1. A. Fukuda, C. Han, K. HakamadaEffort-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.
  2. C. Han, K. Tsuge, H. IbaApplication 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.
  3. C. Han, K. Tsuge, H. IbaOptimization of Artificial Operon Construction by Consultation Algorithms Utilizing LCS, In IEEE Congress on Evolutionary Computation (CEC), Vancouver, Canada, July 2016.