| 14:00-14:10 |
Jinwook Choi, M.D.
Seoul National University Hospital |
Opening Remarks |
|
| 14:10-14:20 |
Kyoungbun Lee, M.D.
Seoul National University Hospital |
Keynote speech: Clinical Reviews on PAIP2019 Challenge: View of Pathologists |
|
| 14:20-14:30 |
Won-Ki Jeong,
Ulsan National Institute of Science and Technology |
Keynote speech: PAIP2019 Challenge Technical Reviews |
|
| 14:30-14:37 |
Team DAISYlab@UKE |
msYI-Net: A multi-scale multi-encoder |
|
| 14:38-14:45 |
Team LRDE |
Segmentation of viable liver tumor using transfer learning |
|
| 14:46-14:53 |
Team blackbear |
Ensemble Learning for Liver Cancer Segmentation and Tumor Burden Estimation |
|
| 14:54-15:01 |
Team Sig-Ipath |
Sig-IPath in PAIP2019 |
|
| 15:02-15:09 |
Team COSYPath |
Liver Cancer Segmentation on Whole Slide Histopathology Images Using a SegNet Ensemble |
|
| 15:10-15:17 |
Team CUHK-Med |
A Robust and Efficient Approach towards Liver Whole Slide Image Segmentation and Viable Burden Estimation |
|
| 15:18-15:25 |
Team QuIIL |
Cascaded deep convolutional neural networks for the assessment of the viable tumor burden in liver cancer |
|
| 15:26-15:33 |
Team Damo AIC |
Multi-task Ensemble Network for Pathological Segmentation of Liver Tumors |
|
| 15:34-15:41 |
Team MIRL-IITM |
Segmentation of viable tumor in whole slide images of liver cancer using an ensemble of diverse deep learning architectures and estimation of the whole tumor region using convex contouring on viable tumor |
|
| 15:42-15:49 |
Team Sen |
Automatic Hepatocellular Carcinoma Detection in Whole-Slide Image using an Ensemble Convolution Neural Network |
|
| 15:50-15:57 |
Hyun Jung |
Training Deep Neural Networks for Liver Cancer Segmentation and Viable Tumor Burden Estimation |
|
| 15:58- |
PAIP 2019 Organizer |
Award Ceremony and Open Discussion |
|
