| --- |
| 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).* |
|
|