Medical image classification using deep learning W Wang, D Liang, Q Chen, Y Iwamoto, XH Han, Q Zhang, H Hu, L Lin, ... Deep learning in healthcare: paradigms and applications, 33-51, 2020 | 97 | 2020 |
Classification of focal liver lesions using deep learning with fine-tuning W Wang, Y Iwamoto, X Han, YW Chen, Q Chen, D Liang, L Lin, H Hu, ... Proceedings of the 2018 International Conference on digital medicine and …, 2018 | 35 | 2018 |
Deep learning-based radiomics models for early recurrence prediction of hepatocellular carcinoma with multi-phase CT images and clinical data W Weibin, C Qingqing, Y Iwamoto, HAN Xianhua, Q Zhang, HU Hongjie, ... 2019 41st Annual International Conference of the IEEE Engineering in …, 2019 | 30 | 2019 |
Deep fusion models of multi-phase CT and selected clinical data for preoperative prediction of early recurrence in hepatocellular carcinoma W Wang, Q Chen, Y Iwamoto, P Aonpong, L Lin, H Hu, Q Zhang, ... IEEE Access 8, 139212-139220, 2020 | 15 | 2020 |
Hand-crafted and deep learning-based radiomics models for recurrence prediction of non-small cells lung cancers P Aonpong, Y Iwamoto, W Wang, L Lin, YW Chen Innovation in Medicine and Healthcare: Proceedings of 8th KES-InMed 2020 …, 2020 | 13 | 2020 |
Phase Attention Model for Prediction of Early Recurrence of Hepatocellular Carcinoma with Multi-phase CT Images and Clinical Data W Wang, F Wang, Q Chen, S Ouyang, Y Iwamoto, X Han, L Lin, H Hu, ... Frontiers in radiology 2, 856460, 2022 | 7 | 2022 |
Residual multilayer perceptrons for genotype-guided recurrence prediction of non-small cell lung cancer Y Ai, P Aonpong, W Wang, Y Li, Y Iwamoto, X Han, YW Chen 2022 44th Annual International Conference of the IEEE Engineering in …, 2022 | 2 | 2022 |
A Transformer-Based Model for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma with Muti-modality MRI G Zhan, F Wang, W Wang, Y Li, Q Chen, H Hu, YW Chen Asian Conference on Computer Vision, 185-194, 2022 | 1 | 2022 |
Deep Learning-based Risk Prediction Model for Recurrence-free Survival in Patients with Hepatocellular Carcinoma Using Multi-phase CT Image W Wang, F Wang, Y Yang, Y Li, J Liu, X Han, L Lin, R Tong, H Hu, ... 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE), 926-929, 2022 | 1 | 2022 |
A Unified Framework for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma with Multi-phase CT Images S Ouyang, Y Xu, W Wang, Y Li, F Wang, Q Chen, L Lin, YW Chen, H Hu Innovation in Medicine and Healthcare: Proceedings of 10th KES-InMed 2022 …, 2022 | 1 | 2022 |
Computer-aided diagnosis of liver cancers using deep learning with fine-tuning W Wang, D Liang, L Lin, H Hu, Q Zhang, Q Chen, X Han, YW Chen IEICE Technical Report; IEICE Tech. Rep. 118 (219), 139-140, 2018 | 1 | 2018 |
ModalityFormer for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma Using Multimodality MRI or CT and Clinical Data Z Gan, W Fang, LI Yinhao, W Weibin, C Qingqing, LIN Lanfen, ... IIEEJ transactions on image electronics and visual computing 11 (2), 30-37, 2023 | | 2023 |
Deep Learning-based Prediction Models of Early Recurrence and Recurrence-free Survival in Hepatocellular Carcinoma with Multi-phase CT W Weibin | | 2022 |
Prediction Model of Early Recurrence of Hepatocellular Carcinoma Based on Deep Learning with Attention Module W Wang, F Wang, Q Chen, Y Iwamoto, X Han, Y Chen IEICE Technical Report; IEICE Tech. Rep. 121 (304), 195-198, 2021 | | 2021 |
Genomics-Based Models for Recurrence Prediction of Non-small Cells Lung Cancers P Aonpong, Y Iwamoto, W Wang, L Lin, YW Chen Innovation in Medicine and Healthcare: Proceedings of 9th KES-InMed 2021, 41-49, 2021 | | 2021 |
Prediction of microvascular invasion in hepatocellular carcinoma based on radiomics method using MRI W Wang, Q Chen, F Wang, Y Iwamoto, X Han, H Hu, L Lin, YW Chen IEICE Technical Report; IEICE Tech. Rep. 120 (156), 25-26, 2020 | | 2020 |
Deep Learning-based Risk Prediction Model for Disease-free Survival in Patients with Hepatocellular Carcinoma Using Multi-phase CT Image W Wang, F Wang, Y Yang, Y Li, X Han, L Lin, R Tong, H Hu, Y Chen IEICE Technical Report; IEICE Tech. Rep., 0 | | |