Provide theorethical introduction about

[jakubMitura14]

1. Introduction to Medical Imaging Theory and Spatial Metadata
   Provide an introductory overview of medical imaging formats and spatial metadata.
   Explain the fundamental concepts and principles underlying medical imaging.
2. Give a theoretical overview of functionalities under development, excluding usage examples.
3. Describe various image transformation functions, including:
   Brightness transform
   Contrast augmentation transform
   Gamma Transform
   Gaussian noise transform
   Rician noise transform
   Mirror transform
   Scale transform
   Gaussian blur transform
   Simulate low-resolution transform
   Elastic deformation transform
   4.Explain the concept of K-fold cross-validation and its relevance in model evaluation.
4. Discuss the concept of probabilistic oversampling and its application in image segmentation tasks.
   Explain how probabilistic oversampling techniques can enhance the robustness of segmentation models.
5. Describe the importance of standardizing image spacing, origin, and orientation.
6. Explain Largest Component Analysis utility in identifying and analyzing the largest connected components within datasets.
7. Give a brief introduction to hyperparameter tuning techniques.
   Explain the usage of the Hyperopt package for automated hyperparameter optimization.

Contact

If you will be intrested contact me on Julia slack (Jakub Mitura) so I can support your effort, explain better what it is about , and clarify any issues :)