5 citations · 6 across the 4 of their papers we have counts for
4 papers
MultiDiffNet: A Multi-Objective Diffusion Framework for Generalizable Brain Decoding
Mengchun Zhang, Kateryna Shapovalenko, Yucheng Shao +2
Neural decoding from electroencephalography (EEG) remains fundamentally limited by poor generalization to unseen subjects, driven by high inter-subject variability and the lack of…
Expert Validation of Synthetic Cervical Spine Radiographs Generated with a Denoising Diffusion Probabilistic Model
Austin A. Barr, Brij S. Karmur, Anthony J. Winder +11
Machine learning in neurosurgery is limited by challenges in assembling large, high-quality imaging datasets. Synthetic data offers a scalable, privacy-preserving solution. We eval…
Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generation
Austin A. Barr, Robert Rozman, Eddie Guo
We propose a new framework for zero-shot generation of synthetic tabular data. Using the large language model (LLM) GPT-4o and plain-language prompting, we demonstrate the ability…
Zero-shot generation of synthetic neurosurgical data with large language models
Austin A. Barr, Eddie Guo, Emre Sezgin
Clinical data is fundamental to advance neurosurgical research, but access is often constrained by data availability, small sample sizes, privacy regulations, and resource-intensiv…