Model Card for Model ID
Model Details
Model Description
- Developed by: MAHAMAT YOUSSOUF
- Funded by [optional]: Self-funded / Personal project
- Shared by [optional]: [More Information Needed]
- Model type: Transformer-based Text-to-Text model (T5-base fine-tuned for summarization)
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model [optional]: t5-base
Model Sources [optional]
- Repository: https://huggingface.co/moudy93/radiology-summarization-t5-lora
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
Summarization of radiology reports (e.g., converting long findings into concise clinical summaries)
Assisting healthcare NLP pipelines for report abstraction
Educational use for NLP model fine-tuning demonstrations
Downstream Use [optional]
Integration into clinical decision support systems (non-diagnostic support only)
Use in Retrieval-Augmented Generation (RAG) pipelines for medical QA
Preprocessing step for structured medical data extraction
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
The model inherits biases from the training dataset (radiology reports may reflect demographic or institutional bias)
Limited generalization outside radiology domain
May generate hallucinated or incomplete summaries
Not suitable for real-world clinical decision-making without expert oversight
Performance depends heavily on input formatting and report style
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-Validate outputs with domain experts before use in healthcare contexts
-Avoid using outputs as standalone medical advice
-Monitor for hallucinations and factual inconsistencies
-Fine-tune further if adapting to different medical datasets
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
input_text = "summarize: The lungs are clear. No focal consolidation, pleural effusion, or pneumothorax."
inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
outputs = model.generate(**inputs, max_length=64)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(summary)
Training Details
Training Data
"NLMCXR_reports" / "ecgen-radiology"
Training Data
Dataset: Radiology report dataset (NLMCXR_reports/ecgen-radiology dataset)
Size: ~3995 samples
Input: Full radiology findings
Target: Corresponding summaries/impressions
Training Procedure
Preprocessing [optional]
Removed XML/HTML tags and special characters
Normalized whitespace and lowercased text
Added task prefix: "summarize:"
Tokenization using T5 tokenizer with truncation and padding
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
Held-out validation/test split from the same dataset 10%
Factors
-Input length variability
-Clinical terminology complexity
-Report structure differences
Metrics
ROUGE-1
ROUGE-2
ROUGE-L
Results
ROUGE-1: ~0.56
ROUGE-2: 0.46
ROUGE-L: 0.56
[More Information Needed]
Results
[More Information Needed]
Summary
-The model performs well on structured radiology reports
-Struggles with rare conditions or highly unstructured text
-Generates concise and readable summaries in most cases
Technical Specifications
Model Architecture and Objective
Encoder-decoder Transformer (T5)
Objective: Sequence-to-sequence text generation (summarization)
Compute Infrastructure
Single GPU training environment
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
-Python3
-PyTorch
-Hugging Face Transformers
-Datasets library
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
Framework versions
