Attribution and modification notice

Original material

DeepDeWedge Tutorial Data
Creator: Simon Wiedemann
DOI: https://doi.org/10.6084/m9.figshare.25043435.v1
Figshare file: tutorial_data.zip, file id 45582309
Checkpoint member: tutorial_data/fitted_model.ckpt
License: Creative Commons Attribution 4.0 International
License URI: https://creativecommons.org/licenses/by/4.0/

The original checkpoint accompanies:

Simon Wiedemann and Reinhard Heckel, “A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography,” Nature Communications 15, 8255 (2024). https://doi.org/10.1038/s41467-024-51438-y

The upstream DeepDeWedge implementation is available at https://github.com/MLI-lab/DeepDeWedge and was reviewed at revision 072075692a44a8f17394214369e6e762abe52bc3.

Changes made by scitomo

The original PyTorch Lightning checkpoint was converted into a scitomo-native package:

executable/pickled training metadata was excluded;
tensor state was exported in Safetensors format;
56 vendor state names were mapped through a reviewed explicit mapping to the native scitomo UNet3D state;
the vendor unet. namespace was removed;
the second bottleneck convolution was mapped from vendor sequence index 2 to the semantically equivalent native sequence index 4;
two learned normalization values changed storage role from non-trainable parameters to native buffers without changing their values; and
strict construction, inference, conversion, validation, provenance, and tensor-inventory records were added.

No tensor value was intentionally changed. Synthetic forward output was bit-exact, and a frozen real tutorial-volume crop passed the predetermined relative-L2 parity threshold.

The converted package is distributed under the source material's CC BY 4.0 terms. No endorsement by Simon Wiedemann, Reinhard Heckel, the Machine Learning and Information Processing Laboratory, Nature Communications, or Figshare is stated or implied.