4 citations · 8 across the 10 of their papers we have counts for
6 papers · 1 filter
Cyst-X: A Multi-Center MRI Benchmark and Federated Learning Framework for Malignancy-Risk Stratification of Pancreatic Cystic Neoplasm
Hongyi Pan, Gorkem Durak, Elif Keles +27
Pancreatic cancer is projected to be the second-deadliest cancer by 2030, making early detection critical. Intraductal papillary mucinous neoplasms (IPMNs), key cancer precursors,…
VHU-Net: Variational Hadamard U-Net for Body MRI Bias Field Correction
Xin Zhu, Ahmet Enis Cetin, Gorkem Durak +13
Bias field artifacts in magnetic resonance imaging (MRI) scans introduce spatially smooth intensity inhomogeneities that degrade image quality and hinder downstream analysis. To ad…
IPMN Risk Assessment under Federated Learning Paradigm
Hongyi Pan, Ziliang Hong, Gorkem Durak +17
Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop…
Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation
Linkai Peng, Zheyuan Zhang, Gorkem Durak +4
Pancreatic cancer remains one of the leading causes of cancer-related mortality worldwide. Precise segmentation of pancreatic tumors from medical images is a bottleneck for effecti…
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
Zheyuan Zhang, Elif Keles, Gorkem Durak +35
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is…
Radiomics Boosts Deep Learning Model for IPMN Classification
Lanhong Yao, Zheyuan Zhang, Ugur Demir +14
Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their ri…