Updating Readme

README.md

@@ -4,23 +4,14 @@ Code accompanying our ISBI 2021 paper "Multimodal Fusion using Sparse CCA \\for
 
 #### Code
 
-The code for CCA is in MATLAB, and that for prediction is in Python. The code is structured as follows
+The code for CCA is in MATLAB, and that for prediction is in Python. The code is structured as follows:
 
-#### Data
-The original dataset can be downloaded from the TCIA repository [here](https://wiki.cancerimagingarchive.net/display/Public/NSCLC+Radiogenomics).
+- ```code```: Contains all the functions to run the CCA methods, the deflation methods, the K-SCCA and K-GCCA code, and code to simulate and evaluate metrics on simulated data.
+- ```data```: This directory should contain the data downloaded from https://uofi.box.com/s/pvhs03t8u9wfjs6j31c6wbvec7237wfc downloaded to subdirectories ```data/brca``` and ```data/simulated``` respectively.
+- ``` results```: This directory contains all the results from our experiments reported in the conference paper. 
+- ``` run```:  All code to re-run our experiments are provided in this directory. 
 
-We also provide a curated dataset containing the images and segmentations we generated and used. The segmentations were originally available in the XML format, which we have converted to NIFTI files of the same size as the original CT image for easy re-use. We also provide the splits we used in our experiments for use in comparison studies. 
+#### Instruction to run code
 
-The curated dataset used in our experiments can be downloaded from [here](https://uofi.box.com/v/nsclc-radiogenomics-curated). This dataset contains
+Here, we provide the details to re-run the code provided in the ```run``` directory.
 
-- ``images``: The studies from the original dataset which were used to generate the original segmentations in the folder
-- ``segmentations``: The segmentations converted to NIFTI files from the AIM annotaion XMLs provided in the original dataset
-- ``full_clinical_file.csv``: The original CSV file contain all the clinical information
-- ``recurrence_splits``: The folder contain all the 5 splits we used in our experiments. Each split file contains the data in rows where each row corresponds to patient_ID | num_of_days | recurrence_bool. The patient_ID is the same as that provided in the original dataset. The value recurrence_bool is 1 if the patient's cancer recurred within the study period, and 0 otherwise. The num_of_days refers to the number of days between the CT image and the recurrence, if the corresponding recurrence_bool is 1. The num_of_days refers to the number of days between the CT image and the day the patient was last tracked in the study. 
-
-#### Code
-
-##### Linear Models
--> can be run from ````linear_cox.ipynb````
-
-Other code will be posted here soon. Stay tuned!