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--- |
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language: |
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- en |
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metrics: |
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- rouge-l |
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tags: |
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- medical |
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- summarization |
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- clinical |
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- bart |
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- Radiology |
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- Radiology Reports |
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datasets: |
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- MIMIC-III |
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widget: |
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- >- |
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post contrast axial sequence shows enhancing large neoplasm left parietal |
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convexity causing significant amount edema mass effect study somewhat limited |
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due patient motion similar enhancing lesion present inferior aspect right |
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cerebellar hemisphere right temporal encephalomalacia noted mra brain shows |
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patent flow anterior posterior circulation evidence aneurysm vascular |
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malformation |
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- >- |
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seen hypodensity involving right parietal temporal lobes right cerebellar |
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hemisphere effacement sulci mild mass effect lateral ventricle hemorrhage new |
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region territorial infarction basal cisterns patent mucosal thickening fluid |
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within paranasal sinuses aerosolized secretions likely related intubation |
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mastoid air cells middle ear cavities clear |
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- >- |
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heart size normal mediastinal hilar contours unchanged widening superior |
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mediastinum likely due combination mediastinal lipomatosis prominent thyroid |
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findings unchanged compared prior ct aortic knob mildly calcified pulmonary |
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vascularity engorged patchy linear opacities lung bases likely reflect |
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atelectasis focal consolidation pleural effusion present multiple old |
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rightsided rib fractures |
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inference: |
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parameters: |
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max_length: 350 |
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--- |
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# Radiology Report Summarization |
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This model summarizes radiology findings into accurate, informative impressions to improve radiologist-clinician communication. |
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## Model Highlights |
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- **Model name:** Radiology_Bart |
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- **Author:** [Muhammad Bilal](linkedin.com/in/muhammad-bilal-6155b41aa) |
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- **Model type:** Sequence-to-sequence model |
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- **Library:** PyTorch, Transformers |
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- **Language:** English |
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### Parent Model |
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- **Repository:** [GanjinZero/biobart-v2-base](https://huggingface.co/GanjinZero/biobart-v2-base) |
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- **Paper:** [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf) |
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This model is a version of pretrained BioBart-v2-base model further finetuned on 70,000 radiology reports to generate radiology impressions. It produces concise, coherent summaries while preserving key findings. |
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## Model Architecture |
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Radiology_Bart is built on the BioBart architecture, a sequence-to-sequence model which is pre-trained on biomedical-text-data[PubMed](https://pubmed.ncbi.nlm.nih.gov/). The encoder-decoder structure allows it to compress radiology findings into impression statements. |
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Key components: |
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- Encoder: Maps input text to contextualized vector representations |
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- Decoder: Generates output text token-by-token |
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- Attention: Aligns relevant encoder and decoder hidden states |
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## Data |
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The model was trained on 70,000 deidentified radiology reports split into training (52,000), validation (8,000), and test (10,000) sets. The data covers diverse anatomical regions and imaging modalities (X-ray, CT, MRI). |
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## Training |
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- Optimization: AdamW |
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- Batch size: 16 |
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- Learning rate: 5.6e-5 |
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- Epochs: 4 |
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The model was trained to maximize the similarity between generated and reference impressions using ROUGE metrics. |
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## Performance |
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**Evaluation Metrics** |
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| ROUGE-1 score | ROUGE-2 score | ROUGE-L score | ROUGELSUM score | |
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|---------------|---------------|---------------|-----------------| |
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| 44.857 | 29.015 | 42.032 | 42.038 | |
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Demonstrating high overlap with human references. |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Pipeline |
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# Sample findings |
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findings = "There is a small lung nodule in the right upper lobe measuring 6 mm. The heart size is normal. No pleural effusion or pneumothorax." |
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# Load model & tokenizer |
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summarizer = pipeline("summarization", model="Mbilal755/Radiology_Bart") |
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tokenizer = AutoTokenizer.from_pretrained("Mbilal755/Radiology_Bart") |
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# Tokenize findings |
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inputs = tokenizer(findings, return_tensors="pt") |
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# Generate summary |
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summary = summarizer(findings)[0]['summary_text'] |
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# Print outputs |
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print(f"Findings: {findings}") |
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print(f"Summary: {summary}") |
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``` |
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## Limitations |
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This model is designed solely for radiology report summarization. It should not be used for clinical decision-making or other NLP tasks. |
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## Check Demo |
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[For Demo Click here](https://huggingface.co/spaces/Mbilal755/Rad_Summarizer) |
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## Model Card Contact |
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- Name: Eng. Muhammad Bilal |
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- [Muhammad Bilal Linkedin](linkedin.com/in/muhammad-bilal-6155b41aa) |
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- [Muhammad Bilal GitHub](https://github.com/BILAL0099) |
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