Paul Friedrich
PhD Student
Center for medical Image Analysis & Navigation (CIAN)
University of Basel
PhD Student
Center for medical Image Analysis & Navigation (CIAN)
University of Basel
I am a third year PhD student at the Center for medical Image Analysis and Navigation (CIAN) of the University of Basel. My work is supervised by Philippe Cattin and Florian Thieringer.
I'm interested in medical image analysis, implicit neural representations, 3D deep learning and generative modeling. My current research is part of the MIRACLE II project and focuses on conditional shape generation and manipulation tasks as well as (un)conditional 3D medical image generation. I'm particularly interested in different types of 3D data representations, including voxel grids, point clouds, meshes, triplanes, and neural implicit representations.
Our paper Generating 3D Pseudo-Healthy Knee MR Images to Support Trochleoplasty Planning which has previously been early-accepted to IPCAI has been published in the International Journal of Computer Assisted Radiology and Surgery.
Our paper MedFuncta: Modality-Agnostic Representations Based on Efficient Neural Fields is available on arXiv. We introduce a modality agnostic continuous data representation based on meta-learned neural fields (NFs) and show how NFs can be generalized from single instances to large datasets. We further demonstrate that we can solve relevant downstream tasks by applying nerual networks to this permutation-equivariant functional representation.
Our review chapter Deep Generative Models for 3D Medical Image Synthesis has been published in Generative Machine Learning Models in Medical Image Computing. We review deep generative models (VAEs, GANs, and DDMs) for 3D medical image synthesis, covering basic principles, advances, and application for relevant downstream tasks. The chapter also addresses evaluation metrics for image fidelity, diversity, utility, and privacy.
A collection of my published research articles. First-author contributions are highlighted.
A collection of my public GitHub repositories.
PyTorch implementation for "MedFuncta: Modality-Agnostic Representations Based on Efficient Neural Fields" (2025)
PyTorch implementation for "WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis" (DGM4MICCAI 2024)
PyTorch implementation for "cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis" (BraTS 2024)
PyTorch implementation for "Point Cloud Diffusion Models for Automatic Implant Generation" (MICCAI 2023)
2024 – “Diffusion Models for Medical Image Analysis”
Technical University of Munich, Lab for AI in Medicine, Daniel Rückert