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View Code? Open in Web Editor NEWDL based representation learning of MS imaging data
License: GNU General Public License v3.0
DL based representation learning of MS imaging data
License: GNU General Public License v3.0
For the hyperparameter tuning, the following things should be considered/optimized:
Next big issue Enforce dissimilarity across datasets for features being dissimilar within datasets:
Shared node embedding (ions are nodes) from coloc networks across multiple graphs (datasets).
As another model for included in the evaluation.
Potentially using embeddings as conditions, to integrate additional datasets later that were not part of training. I made a simple reference implementation of a scPoli like model: https://github.com/tdrose/lightning-cvae
As an additional model for the package. Use previous work from Dominik for the dataloaders.
Try to incorporate intensities instead of just thresholds.
For the evaluation of the project, we discussed a few scenarios from simple to more complex. Dataloaders (training, validation, and testing) for each scenario should be implemented.
Move the current torch implementation to lightning.
The is room for improvement in the CRL model, by adapting the model architecture and trying out different loss functions
What should definitely be explored:
Notes:
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