update MIMIC-IR section (#1)

- update MIMIC-IR section (f06313cf6606a0af6da16fd39cc5bfef36f58669)


Co-authored-by: zhang <[email protected]>

README.md

@@ -7,31 +7,47 @@ This is the official data repository for [RadIR: A Scalable Framework for Multi-
 
 We mine image-paired report to extract findings on diverse anatomy structures, and quantify the multi-grained image-image relevance via [RaTEScore](https://arxiv.org/abs/2406.16845). 
 Specifically, we have extended two public datasets for multi-grained medical image retrieval task:
-- MIMIC-IR is extended from [MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/), containing 377,110 images and x anatomy structures.
+- 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.
 
-A simple demo to read the data from CTRATE-IR:
+**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:
+```
+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:
+```python
 import pandas as pd
 import numpy as np
 
+# CTRATE-IR
 anatomy_condition = 'bone'
 sample_A_idx = 10
 sample_B_idx = 20
-
 df = pd.read_csv(f'CTRATE-IR/anatomy/train_entity/{anatomy_condition}.csv')
 id_ls = df.iloc[:,0].tolist()
 findings_ls = df.iloc[:,1].tolist()
 
 simi_tab = np.load(f'CTRATE-IR/anatomy/train_ratescore/{anatomy_condition}.npy')
 
+# # MIMIC-IR
+# anatomy_condition = 'lungs'
+# sample_A_idx = 10
+# sample_B_idx = 20
+# df = pd.read_csv(f'MIMIC-IR/anatomy/train_caption/{anatomy_condition}.csv')
+# id_ls = df.iloc[:,0].tolist()
+# findings_ls = df.iloc[:,1].tolist()
+# simi_tab = np.load(f'MIMIC-IR/anatomy/train_ratescore/{anatomy_condition}.npy')
+
 print(f'Sample {id_ls[sample_A_idx]} findings on {anatomy_condition}: {findings_ls[sample_A_idx]}')
 print(f'Sample {id_ls[sample_B_idx]} findings on {anatomy_condition}: {findings_ls[sample_B_idx]}')
 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:
-```
+```python
 import os
 import json
 import numpy as np
@@ -39,21 +55,42 @@ import numpy as np
 sample_A_idx = 10
 sample_B_idx = 20
 
+# CTRATE-IR
 with open('CTRATE-IR/train_filtered.jsonl', 'r') as f:
   data = f.readlines()
   data = [json.loads(l) for l in data]
-
 simi_tab = np.load(f'CTRATE-IR/CT_train_ratescore.npy')
-
 sample_A_id = os.path.basename(data[sample_A_idx]['img_path'])
 sample_B_id = os.path.basename(data[sample_B_idx]['img_path'])
-
 sample_A_report = os.path.basename(data[sample_A_idx]['text'])
 sample_B_report = os.path.basename(data[sample_B_idx]['text'])
 
+# # MIMIC-IR
+# data = pd.read_csv('MIMIC-IR/val_caption.csv')
+# simi_tab = np.load('MIMIC-IR/val_ratescore.npy')
+# sample_A_id = data.iloc[sample_A_idx]['File Path']
+# sample_B_id = data.iloc[sample_B_idx]['File Path']
+# sample_A_report = data.iloc[sample_A_idx]['Findings']
+# sample_B_report = data.iloc[sample_B_idx]['Findings']
+
 print(f'Sample {sample_A_id} reports: {sample_A_report}\n')
 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.
+
+
+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.
+
+
+**Citation**
+
+If you find our data useful, please consider citing our work!
+```
+@article{zhang2025radir,
+  title={RadIR: A Scalable Framework for Multi-Grained Medical Image Retrieval via Radiology Report Mining},
+  author={Zhang, Tengfei and Zhao, Ziheng and Wu, Chaoyi and Zhou, Xiao and Zhang, Ya and Wang, Yangfeng and Xie, Weidi},
+  journal={arXiv preprint arXiv:2503.04653},
+  year={2025}
+}
+```