Model Card for Model ID

The model can be used to generate accurate and informative radiology impressions, which can help to improve communication between radiologists and other healthcare providers.

Model Details

Model Description

This model is a BioBart-based sequence-to-sequence model that was trained on the custom dataset to summarize radiology findings into impressions. In training i use 70000 radiology reports to train the model to summarize radiology findings into impressions.

Developed by: [Engr. Hamza Iqbal Malik (UET TAXILA)]
Shared by : [Engr. Hamza Iqbal Malik (UET TAXILA)]
Model type: [Medical Text Summarization Model]
Language(s) (NLP): [English]
Finetuned from model: [GanjinZero/biobart-v2-base]

Model Sources

Repository: [GanjinZero/biobart-v2-base]
Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model]
Demo: [hamzamalik11/radiology_summarizer]

Uses

The model is intended to be used for generating radiology impressions. The model should not be used for any other purpose.

Direct Use

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.

Out-of-Scope Use

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.

Recommendations

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.

How to Get Started with the Model

Use the code below to get started with the model. from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoModelForSeq2SeqLM from transformers import DataCollatorForSeq2Seq

model_checkpoint = "attach your trained model here"

model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint) tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) from transformers import SummarizationPipeline

summarizer = SummarizationPipeline(model=model, tokenizer=tokenizer)

output= summarizer("heart size normal mediastinal hilar contours remain stable small right pneumothorax remains unchanged surgical lung staples overlying left upper lobe seen linear pattern consistent prior upper lobe resection soft tissue osseous structures appear unremarkable nasogastric endotracheal tubes remain satisfactory position atelectatic changes right lower lung field remain unchanged prior study")

Training Details

Training Data

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. 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.

Training Procedure

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.

Training Hyperparameters

Training regime: -[evaluation_strategy="epoch"], -[learning_rate=5.6e-5], -[per_device_train_batch_size=batch_size //4], -[per_device_eval_batch_size=batch_size //4,] -[weight_decay=0.01], -[save_total_limit=3], -[num_train_epochs=num_train_epochs //4], -[predict_with_generate=True //4], -[logging_steps=logging_steps], -[push_to_hub=False]

Evaluation

Testing Data, Factors & Metrics

Testing Data

The testing data consisted of 10,000 radiology reports.

Factors

The following factors were evaluated: [-ROUGE-1] [-ROUGE-2] [-ROUGE-L] [-ROUGELSUM]

Metrics

The following metrics were used to evaluate the model: -[ROUGE-1 score: 44.857] -[ROUGE-2 score: 29.015] -[ROUGE-L score: 42.032] -[ROUGELSUM score: 42.038]

Results

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.

Summary

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.

Model Card Authors

-Name: Engr. Hamza Iqbal Malik
-LinkedIn: www.linkedin.com/in/hamza-iqbal-malik-42366a239
-GitHub: https://github.com/hamza4344

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