Update README.md

README.md

@@ -10,6 +10,7 @@ Specifically, we have extended two public datasets for multi-grained medical ima
 - MIMIC-IR is extended from [MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/), containing 377,110 images and 90 anatomy structures.
 - CTRATE-IR is extended from [CTRATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE), containing 25,692 images and 48 anatomy structures.
 
+
 **Note:** For the MIMIC-IR dataset, you need to manually merge and decompress the files.  
 After downloading all split parts (from `MIMIC-IR.tar.gz.part00` to `MIMIC-IR.tar.gz.part08`), execute the following commands in the same directory:
 ```
@@ -17,7 +18,8 @@ cat MIMIC-IR.tar.gz.part* > MIMIC-IR.tar.gz
 tar xvzf MIMIC-IR.tar.gz
 ```
 
-A simple demo to read the data from CTRATE-IR:
+
+A simple demo to read the data (take CTRATE-IR for instance):
 ```python
 import pandas as pd
 import numpy as np
@@ -46,7 +48,9 @@ print(f'Sample {id_ls[sample_B_idx]} findings on {anatomy_condition}: {findings_
 print(f'Relevance score: {simi_tab[sample_A_idx, sample_B_idx]}')
 ```
 
-Note that the score have been normalized to 0~100 and stored in uint8. We also provide the whole image-level relevance quantified based on their entire reports:
+**Note:** the score have been normalized to 0~100 and stored in uint8. 
+
+We also provide the whole image-level relevance quantified based on their entire reports:
 ```python
 import os
 import json
@@ -78,8 +82,6 @@ print(f'Sample {sample_B_id} reports: {sample_B_report}\n')
 print(f'Whole image relevance score: {simi_tab[sample_A_idx, sample_B_idx]}')
 ```
 
-
-
 For raw image data, you can download them from [CTRATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE) (or [RadGenome-ChestCT](https://huggingface.co/datasets/RadGenome/RadGenome-ChestCT)) and [MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/). We keep all the sample id consistent so you can easily find them.