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--- |
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language: |
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- en |
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metrics: |
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- rouge |
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library_name: transformers |
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pipeline_tag: summarization |
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tags: |
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- medical |
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- radiology |
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- biobart |
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- impression |
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- findings |
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- biomedical |
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- bart |
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--- |
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# Model Card for Model ID |
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The model can be used to generate accurate and informative radiology impressions, which can help to improve communication between radiologists and other healthcare providers. |
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## Model Details |
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### Model Description |
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This model is a BioBart-based sequence-to-sequence model that was trained on the custom dataset to summarize radiology findings into impressions. |
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In training i use 70000 radiology reports to train the model to summarize radiology findings into impressions. |
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- **Developed by:** [Engr. Hamza Iqbal Malik (UET TAXILA)] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [Medical Text Summarization Model] |
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- **Language(s) (NLP):** [English] |
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- **Finetuned from model [optional]:** [GanjinZero/biobart-v2-base] |
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### Model Sources |
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- **Repository:** [GanjinZero/biobart-v2-base] |
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- **Paper:** [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model] |
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- **Demo:** [hamzamalik11/radiology_summarizer] |
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## Uses |
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The model is intended to be used for generating radiology impressions. The model should not be used for any other purpose. |
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### Direct Use |
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The model can be directly used for generating impressions from radiology reports. Users can input the findings of a radiology report, and the model will generate a summarized impression based on that information. |
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### Out-of-Scope Use |
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The model should not be used for any purpose other than generating impressions from radiology reports. It is not suitable for tasks outside of radiology report summarization. |
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### Recommendations |
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Users should be aware of the limitations and potential biases of the model when using the generated impressions for clinical decision-making. Further information is needed to provide specific recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoModelForSeq2SeqLM |
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from transformers import DataCollatorForSeq2Seq |
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model_checkpoint = "attach your trained model here" |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint) |
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) |
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from transformers import SummarizationPipeline |
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summarizer = SummarizationPipeline(model=model, tokenizer=tokenizer) |
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output= summarizer("heart size normal mediastinal hilar contours remain stable small right pneumothorax remains unchanged surgical lung staples overlying |
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left upper lobe seen linear pattern consistent prior upper lobe resection soft tissue osseous structures appear unremarkable nasogastric |
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endotracheal tubes remain satisfactory position atelectatic changes right lower lung field remain unchanged prior study") |
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## Training Details |
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### Training Data |
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The training data was a custom dataset of 70,000 radiology reports.The data was cleaned to remove any personal or confidential information. The data was also tokenized and normalized. |
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The training data was split into a training set and a validation set. The training set consisted of 63,000 radiology reports, and the validation set consisted of 7,000 radiology reports. |
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### Training Procedure |
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The model was trained using the Hugging Face Transformers library: https://huggingface.co./transformers/. The model was trained using the AdamW optimizer with a learning rate of 5.6e-5. The model was trained for 10 epochs. |
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#### Training Hyperparameters |
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- **Training regime:** |
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-[evaluation_strategy="epoch"], |
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-[learning_rate=5.6e-5], |
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-[per_device_train_batch_size=batch_size //4], |
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-[per_device_eval_batch_size=batch_size //4,] |
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-[weight_decay=0.01], |
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-[save_total_limit=3], |
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-[num_train_epochs=num_train_epochs //4], |
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-[predict_with_generate=True //4], |
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-[logging_steps=logging_steps], |
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-[push_to_hub=False] |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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The testing data consisted of 10,000 radiology reports. |
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#### Factors |
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The following factors were evaluated: |
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[-ROUGE-1] |
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[-ROUGE-2] |
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[-ROUGE-L] |
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[-ROUGELSUM] |
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#### Metrics |
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The following metrics were used to evaluate the model: |
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-[ROUGE-1 score: 44.857] |
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-[ROUGE-2 score: 29.015] |
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-[ROUGE-L score: 42.032] |
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-[ROUGELSUM score: 42.038] |
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### Results |
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The model achieved a ROUGE-L score of 42.032 on the testing data. This indicates that the model was able to generate summaries that were very similar to human-written summaries. |
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#### Summary |
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The model was trained on a custom dataset of 70,000 radiology reports. The model was able to achieve a ROUGE-L score of 42.032 on the testing data. This indicates that the model is able to generate summaries that are very similar to human-written summaries. |
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## Model Card Authors |
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-Name: Engr. Hamza Iqbal Malik |
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-LinkedIn: [www.linkedin.com/in/hamza-iqbal-malik-42366a239](www.linkedin.com/in/hamza-iqbal-malik-42366a239) |
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-GitHub: [https://github.com/hamza4344](https://github.com/hamza4344) |
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## Model Card Contact |
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-Name: Engr. Hamza Iqbal Malik |
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-LinkedIn: [www.linkedin.com/in/hamza-iqbal-malik-42366a239](www.linkedin.com/in/hamza-iqbal-malik-42366a239) |
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-GitHub: [https://github.com/hamza4344](https://github.com/hamza4344) |
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