collaborators

12 papers

cs.CV2026

Gaussian Meta-Space Augmentation for Stacking Ensembles in Multimodal IPMN Risk Stratification

Max A. Nelson, Eminenur Sen Tasci, Zhixiang Wang +12

Pancreatic cancer is among the most lethal malignancies; risk stratification of intraductal papillary mucinous neoplasms (IPMNs) offers a crucial opportunity for early intervention…

eess.IV2026

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…

cs.CL2026

Revisiting LLM Adaptation for 3D CT Report Generation: A Study of Scaling and Diagnostic Priors

Vanshali Sharma, Andrea M. Bejar, Halil Ertugrul Aktas +4

Recent advances in multimodal learning, including large language models (LLMs) and vision-language models (VLMs), have demonstrated strong adaptability to natural images. However,…

eess.IV2026

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,…

eess.IV2026

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…

cs.CV2026

CrossPan: A Comprehensive Benchmark for Cross-Sequence Pancreas MRI Segmentation and Generalization

Linkai Peng, Cuiling Sun, Zheyuan Zhang +10

Automatic pancreas segmentation is fundamental to abdominal MRI analysis, yet deep learning models trained on one MRI sequence often fail catastrophically when applied to another-a…