Applications

  1. convexAdam: Fast and accurate optimisation for registration with little learning
  2. denoiseg: Joint Denoising and Segmentation
  3. histolab: WSI processing in deep learning pipelines
  4. histology: pseudo-histology synthesis training from corresponding micro-CT slices on manually registered samples
  5. Active Learning with HRNet: Deep High-Resolution Representation Learning for Visual Recognition
  6. nnunet: Auto-adaptive semantic segmentation method
  7. noise2inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging
  8. pyg: PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data.
  9. registration-autofuse: data-driven fusion strategy for deformable image registration
  10. SAM, SAM++ and Cross-SAM: match arbitrary anatomical landmarks between two radiological images (e.g. CT, MR, X-ray, etc.)
  11. sun: Highly accurate segmentation of large 3D volumes (MDLMAs scaling-the-unet)
  12. voxelmorph: general purpose library for learning-based tools for alignment/registration and modelling with deformations

Publications

  1. Artificial intelligence for synchrotron-radiation tomography: Julian P. Moosmann et al., Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265502, doi: 10.1117/12.2675628
  2. Evaluating Design Choices for Deep Learning Registration Networks: Hanna Siebert et al., Bildverarbeitung für die Medizin, doi: 10.1007/978-3-658-33198-6_26