@inproceedings{CTT100907906,
  author = {Jin Z. and Pang M. and Qu T. and Oshibe H. and Sasage R. and Suzuki K.},
  title = {Feature Map Visualization for Explaining Black-Box Deep Learning Model in Liver Tumor Segmentation},
  booktitle = {},
  year = 2023,
}

@inproceedings{CTT100907904,
  author = {Yang Y. and Jin Z. and Suzuki K.},
  title = {Federated learning - Game changing AI concept to train AI without sending patient data out from hospitals},
  booktitle = {},
  year = 2023,
}

@inproceedings{CTT100907905,
  author = {Yang S. and Xiang M. and Qu T. and Jin Z. and Suzuki K.},
  title = {Reconstruction of Fast Acquisition MRI with Under-sampled K-space Data by Using Massive-Training Artificial Neural Networks (MTANNs)},
  booktitle = {},
  year = 2023,
}

@inproceedings{CTT100907907,
  author = {Yang Y. and Jin Z. and Nakatani F. and Miyake M. and Suzuki K.},
  title = {Development of a small-data deep-learning model based on an MTANN for soft tissue sarcoma diagnosis in MRI},
  booktitle = {},
  year = 2023,
}

@inproceedings{CTT100904973,
  author = {Ze Jin and Maolin Pang and Yuqiao Yang and Fahad Parvez Mahdi and Tianyi Qu and Ren Sasage and Kenji Suzuki},
  title = {Explaining Massive-Training Artificial Neural Networks in Medical Image Analysis Task Through Visualizing Functions Within the Models},
  booktitle = {Lecture notes in computer science, LNCS},
  year = 2023,
}

@inproceedings{CTT100898547,
  author = {Pang M. and Jin Z. and Qu T. and Mahdi F. P. and Sasage R. and Suzuki K.},
  title = {Functional Model Visualization for Explaining Massive-Training Artificial Neural Network for Liver Tumor Segmentation},
  booktitle = {},
  year = 2023,
}

@inproceedings{CTT100891310,
  author = {Deng Z. and Jin Z. and Suzuki K.},
  title = {Radiation Dose Reduction in Digital Breast Tomosynthesis by MTANN with Multi-scale Kernels},
  booktitle = {},
  year = 2023,
}

@inproceedings{CTT100891311,
  author = {Yang Y. and Jin Z. and Nakatani F. and Miyake M. and Suzuki K.},
  title = {AI-aided Diagnosis of Rare Soft-Tissue Sarcoma by Means of Massive-Training Artificial Neural Network (MTANN)},
  booktitle = {},
  year = 2023,
}

@inproceedings{CTT100891312,
  author = {Yang S. and Xiang M. and Qu T. and Jin Z. and Suzuki K.},
  title = {Under-sampled Image Reconstruction in Fast Acquisition MRI with Massive-Training Artificial Neural Networks (MTANNs) Deep Learning Approach},
  booktitle = {},
  year = 2023,
}

@inproceedings{CTT100891309,
  author = {Xu L. and Mahdi F. P. and Jin Z. and Noguchi Y. and Murata M. and Suzuki K.},
  title = {Generating simulated fluorescence images for enhancing proteins from optical microscopy images of cells using massive-training artificial neural networks},
  booktitle = {Proceedings of SPIE},
  year = 2023,
}

@inproceedings{CTT100887257,
  author = {Zhipeng Deng and Ze Jin and Kenji Suzuki},
  title = {Radiation Dose Reduction in Digital Breast Tomosynthesis (DBT) by Means  of Multi-scale-Kernel Massive-Training Artificial Neural Network (MTANN)  for Generating Virtual High-Dose Images},
  booktitle = {},
  year = 2023,
}