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--- |
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tags: |
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- medical |
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--- |
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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). |
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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). |
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Specifically, we have extended two public datasets for multi-grained medical image retrieval task: |
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- MIMIC-IR is extended from [MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/), containing 377,110 images and x anatomy structures. |
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- CTRATE-IR is extended from [CTRATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE), containing 25,692 images and 48 anatomy structures. |
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A simple demo to read the data from CTRATE-IR: |
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``` |
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import pandas as pd |
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import numpy as np |
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anatomy_condition = 'bone' |
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sample_A_idx = 10 |
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sample_B_idx = 20 |
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df = pd.read_csv(f'CTRATE-IR/anatomy/train_entity/{anatomy_condition}.csv') |
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id_ls = df.iloc[:,0].tolist() |
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findings_ls = df.iloc[:,1].tolist() |
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simi_tab = np.load(f'CTRATE-IR/anatomy/train_ratescore/{anatomy_condition}.npy') |
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print(f'Sample {id_ls[sample_A_idx]} findings on {anatomy_condition}: {findings_ls[sample_A_idx]}') |
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print(f'Sample {id_ls[sample_B_idx]} findings on {anatomy_condition}: {findings_ls[sample_B_idx]}') |
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print(f'Relevance score: {simi_tab[sample_A_idx, sample_B_idx]}') |
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``` |
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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: |
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``` |
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import os |
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import json |
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import numpy as np |
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sample_A_idx = 10 |
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sample_B_idx = 20 |
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with open('CTRATE-IR/train_filtered.jsonl', 'r') as f: |
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data = f.readlines() |
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data = [json.loads(l) for l in data] |
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simi_tab = np.load(f'CTRATE-IR/CT_train_ratescore.npy') |
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sample_A_id = os.path.basename(data[sample_A_idx]['img_path']) |
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sample_B_id = os.path.basename(data[sample_B_idx]['img_path']) |
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sample_A_report = os.path.basename(data[sample_A_idx]['text']) |
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sample_B_report = os.path.basename(data[sample_B_idx]['text']) |
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print(f'Sample {sample_A_id} reports: {sample_A_report}\n') |
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print(f'Sample {sample_B_id} reports: {sample_B_report}\n') |
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print(f'Whole image relevance score: {simi_tab[sample_A_idx, sample_B_idx]}') |
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``` |
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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. |
