Julian Wyatt

See Also:
Interests
My research interests are:
- Self-supervised methods
- Anomaly detection
- Generative Models
- Multimodal deep learning
Biography
Julian is a DPhil student in Computer Science, working in the OxMedIS research group. He previously completed an integrated Masters in Computer Science at Durham, which led to his persuit of a DPhil.
His research focuses on self-supervised machine learning methods and applications within the medical domain. Mainly applied to medical landmark detection: a method used by clinicians to produce explainable diagnosis.
Julian's first year culminated in winning the Cephalometric Landmark Detection Challenge @ MICCAI 2024. His approach utilised an RCNN and novel augmentation techniques to align cephalograms from diverse domains.
Prior to Julian's Oxford research he worked with Dr Chris Willcocks and published work on unsupervised anomaly detection with diffusion models at CVPR workshops 2022 (AnoDDPM)
Selected Publications
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Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection
Julian Wyatt and Irina Voiculescu
Winner of the Landmark Detection Challenge at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). October, 2024.
Details about Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection | BibTeX data for Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection | Link to Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection
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Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise
Julian Wyatt‚ Adam Leach‚ Sebastian M Schmon and Chris G Willcocks
Pages 650–656. 2022.
Details about Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise | BibTeX data for Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise | Link to Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise