radiology-coder-sft / README.md
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---
license: apache-2.0
language:
- en
tags:
- medical
- radiology
- icd-10
- report-coding
- clinical-nlp
- imaging
task_categories:
- text-generation
pretty_name: "Radiology Report ICD-10 Coder"
size_categories:
- 10K<n<100K
---
# Radiology Report ICD-10 Coder
Part of the **[AxisMapper Medical AI Suite](https://huggingface.co/collections/AmareshHebbar/axiomapper-medical-ai-suite)** — 16 domain-specific SFT datasets for fine-tuning medical LLMs.
**Built by [AmareshHebbar](https://huggingface.co/AmareshHebbar) | Studio Ilios / Humanova Minds**
---
## What this dataset does
Radiology reports / impressions → ICD-10-CM codes for all documented findings
## Why download this
Automate radiology coding, build report-to-code pipelines for RIS/PACS integration, or train models to extract diagnosis codes from chest X-ray, CT, MRI, and ultrasound impressions.
## Dataset stats
| Split | Rows |
|-------|------|
| Train | 20,072 |
| Validation | 2,509 |
| Test | 2,509 |
| **Total** | **25,090** |
## Data format
Every row is a `messages` list in chat format — compatible with **Unsloth**, **TRL SFTTrainer**, **LLaMA-Factory**, and any OpenAI-style fine-tuning pipeline:
```json
{
"messages": [
{"role": "system", "content": "You are a ..."},
{"role": "user", "content": "IMPRESSION: 1.8cm hypoechoic nodule right thyroid lobe, TIRADS 4. Recommend FNA."},
{"role": "assistant", "content": "ICD-10-CM Coding: E04.1 — Non-toxic single thyroid nodule
Note: Add malignancy code post-biopsy if confirmed."}
]
}
```
## Data source
**AGBonnet/augmented-clinical-notes — 25k radiology-relevant clinical notes filtered by imaging keywords**
→ https://huggingface.co/datasets/AGBonnet/augmented-clinical-notes
All data is extracted from authoritative public sources. No LLM-generated or synthetic content.
## Who should use this
Radiology AI companies, RIS/PACS vendors, hospital coding departments, teleradiology platforms.
## Quick start
```python
from datasets import load_dataset
ds = load_dataset("AmareshHebbar/radiology-coder-sft")
print(ds["train"][0])
```
## Fine-tuning example (Unsloth)
```python
from unsloth import FastLanguageModel
from trl import SFTTrainer
from datasets import load_dataset
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="unsloth/Qwen2.5-3B-Instruct",
max_seq_length=2048,
load_in_4bit=True,
)
dataset = load_dataset("AmareshHebbar/radiology-coder-sft", split="train")
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
train_dataset=dataset,
dataset_text_field="messages",
max_seq_length=2048,
)
trainer.train()
```
## Related datasets in this collection
| Dataset | Task | Rows |
|---------|------|------|
| [icd10-coder-sft](https://huggingface.co/datasets/AmareshHebbar/icd10-coder-sft) | ICD-10-CM coding | 74.7k |
| [symptom-diagnoser-sft](https://huggingface.co/datasets/AmareshHebbar/symptom-diagnoser-sft) | Symptom → diagnosis | 119k |
| [clinical-summarizer-sft](https://huggingface.co/datasets/AmareshHebbar/clinical-summarizer-sft) | SOAP summarization | 30k |
| [discharge-qa-sft](https://huggingface.co/datasets/AmareshHebbar/discharge-qa-sft) | Discharge summary QA | 30k |
| [pmjay-classifier-sft](https://huggingface.co/datasets/AmareshHebbar/pmjay-classifier-sft) | PM-JAY packages | 11.1k |
| [radiology-coder-sft](https://huggingface.co/datasets/AmareshHebbar/radiology-coder-sft) | Radiology coding | 25k |
| [medical-ner-sft](https://huggingface.co/datasets/AmareshHebbar/medical-ner-sft) | Clinical NER | 16.7k |
| [hindi-medical-sft](https://huggingface.co/datasets/AmareshHebbar/hindi-medical-sft) | Hindi medical QA | 19.7k |
## Citation
```bibtex
@misc{axiomapper2026,
author = {Hebbar, Amaresh},
title = {AxisMapper: Medical AI Fine-tuning Dataset Suite},
year = {2026},
publisher = {HuggingFace},
url = {https://huggingface.co/collections/AmareshHebbar/axiomapper-medical-ai-suite}
}
```
---
*AxisMapper is an open-source project. Star the repo, open issues, and contribute at [GitHub](https://github.com/amareshhebbar/AxisMapper).*