#298 Correct docs

docs/getting-started/installation.md

@@ -2,9 +2,16 @@
 
 Our package is offered through a very simple pip install, you can run:
 ```bash
+pip install foundation-cancer-image-biomarker
+```
+The package provides simple ways to interact with the foundation model through the means of several utility scripts and functions. 
+
+If you would like to install the bleeding edge version, please add 
+```bash
 pip install foundation-cancer-image-biomarker --pre
 ```
-The package provides simple ways to interact with the foundation model through the means of several utility scripts and functions. Once you have installed the package, you can move to our [Quick Start](./quick-start.md) guide.
+
+Once you have installed the package, you can move to our [Quick Start](./quick-start.md) guide.
 
 
 ## Notes

docs/getting-started/quick-start.md

@@ -4,10 +4,12 @@
 ## Extracting Features from the Foundation Model (Recommended)
 
 <u>Step 1:</u> Install all our dependencies:
+
 ```bash
-pip install foundation-cancer-image-biomarker --pre
+pip install foundation-cancer-image-biomarker
 ```
 
+
 Fore more info: [See detailed Install instructions](./installation.md)
 
 <u>Step 2:</u>  Generate a CSV file with the path to your images and seed points (in physical coordinates),
@@ -31,8 +33,10 @@ This will preprocess your data, download our pre-trained model and execute infer
     If you want to download the weights separately, you can run download it from [here](https://zenodo.org/records/10528450/files/model_weights.torch?download=1) and place it in the current working directory.
 
 You can test to check if the global coordinates are where you expect by using our provided `visualize_seed_point` function. 
+We expect the coordinates in the LPS coordinate system (like ITK) but if you have it in RAS, you can negate the X and Y coordinates and that should work with our system. See [here](https://discourse.slicer.org/t/converting-fiducial-coordinates-from-ras-to-lps/9707)
+
 ```python
-from fmcib.visualize import visualize_seed_point
+from fmcib.visualization.verify_io import visualize_seed_point
 import pandas as pd
 
 feature_df = pd.read_csv("csv_path_here")