Create README.md

README.md

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+This is the official data repository for [RadIR: A Scalable Framework for Multi-Grained Medical Image Retrieval via Radiology Report Mining](https://www.arxiv.org/abs/2503.04653).
+
+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.
+- 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:
+```
+import pandas as pd
+import numpy as np
+
+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')
+
+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]}')
+```
+
+We also provide the whole image-level relevance quantified based on their entire reports:
+```
+import os
+import json
+import numpy as np
+
+sample_A_idx = 10
+sample_B_idx = 20
+
+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'])
+
+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.