Structure README

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 # Representation of High-Dimensional Cancer Cell Morphodynamics in 2-D Latent Space
 
 [Paper - TBD](https://void)
+
+TODO: Fill in figures
+
+## Overview
+
+### Figure 1
+
+![Latent Representation Pipeline](figures/Figure1.png "Latent Representation Pipeline")
+
+### Examples
+
+#### Spheroid
+
+![Spheroid Invasions](figures/Figure1.png "Spheroid Invasions")
+
+#### Drug Treatments
+
+![CN03](figures/CN03.png "CN03 Drug Treatment")
+
+![Y27632](figures/Y27632.png "Y27632 Drug Treatment")
+
+### Latent Dimensions
+
+![Model Training per Dimension](figures/SI_model_training.png "Model Training")
+
+## Usage
+
+Example notebooks can be found in [notebooks/](notebooks/). Source code can be found in [code/](code/). Briefly, the [Tensorflow](https://www.tensorflow.org/) implementation is found in [code/sunlab/sunflow/](code/sunlab/sunflow) and the [PyTorch](https://pytorch.org/) implementation can be found in [code/sunlab/sunflow/](code/sunlab/suntorch). Environments used can be found in the source Yaml files ready to be used with [Anaconda](https://www.anaconda.com/) or related technology.
+
+## Training
+
+An example of training a standard autoencoder can be found in [notebooks/Autoencoder.ipynb](notebooks/Autoencoder.ipynb).
+
+TODO: More implementations
+
+## Pretrained Model Information
+
+The MaxAbsScaler contains the scaling factors to transform the morphological features to the normalized features. The morphological features were derived from 1024x1024 pixel images on a confocal microscope (0.4NA, 10x objective) with a pixel to micron ratio of ??.