5 citations · 13 across the 17 of their papers we have counts for
18 papers
Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer
Ziliang Hong, Hongyi Pan, Halil Ertugrul Aktas +7
Robust medical image segmentation across imaging modalities is challenging because of large differences in appearance and intensity distributions. Models trained on a single modali…
BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization
Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas +18
Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sites due to vendor-specific acqu…
LUMINA: A Multi-Vendor Mammography Benchmark with Energy Harmonization Protocol
Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas +9
Publicly available full-field digital mammography (FFDM) datasets remain limited in size, clinical annotations, and vendor diversity, hindering the development of robust models. We…
MammoClean: Toward Reproducible and Bias-Aware AI in Mammography through Dataset Harmonization
Yalda Zafari, Hongyi Pan, Gorkem Durak +3
The development of clinically reliable artificial intelligence (AI) systems for mammography is hindered by profound heterogeneity in data quality, metadata standards, and populatio…
Pancreas Part Segmentation under Federated Learning Paradigm
Ziliang Hong, Halil Ertugrul Aktas, Andrea Mia Bejar +15
We present the first federated learning (FL) approach for pancreas part(head, body and tail) segmentation in MRI, addressing a critical clinical challenge as a significant innovati…
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,…
Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation
Hongyi Pan, Ziliang Hong, Gorkem Durak +2
Federated learning enables collaborative training of deep learning models across institutions without sharing sensitive patient data. However, its performance is often limited by s…
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…
VideoAds for Fast-Paced Video Understanding
Zheyuan Zhang, Monica Dou, Linkai Peng +3
Advertisement videos serve as a rich and valuable source of purpose-driven information, encompassing high-quality visual, textual, and contextual cues designed to engage viewers. T…
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…
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI
Hongyi Pan, Gorkem Durak, Zheyuan Zhang +16
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However…
Frequency-Based Federated Domain Generalization for Polyp Segmentation
Hongyi Pan, Debesh Jha, Koushik Biswas +1
Federated Learning (FL) offers a powerful strategy for training machine learning models across decentralized datasets while maintaining data privacy, yet domain shifts among client…
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy
Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma +19
Colonoscopy is the primary method for examination, detection, and removal of polyps. However, challenges such as variations among the endoscopists' skills, bowel quality preparatio…
Ethical Framework for Responsible Foundational Models in Medical Imaging
Debesh Jha, Gorkem Durak, Abhijit Das +33
The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning. Th…
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation
Debesh Jha, Nikhil Kumar Tomar, Koushik Biswas +11
Accurate segmentation of organs from abdominal CT scans is essential for clinical applications such as diagnosis, treatment planning, and patient monitoring. To handle challenges o…
PAM-UNet: Shifting Attention on Region of Interest in Medical Images
Abhijit Das, Debesh Jha, Vandan Gorade +7
Computer-aided segmentation methods can assist medical personnel in improving diagnostic outcomes. While recent advancements like UNet and its variants have shown promise, they fac…
Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers
Hongyi Pan, Emadeldeen Hamdan, Xin Zhu +2
Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initialization remains challenging and co…
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…