| --- |
| pretty_name: RadGraph-IT |
| language: |
| - it |
| task_categories: |
| - token-classification |
| size_categories: |
| - n<1K |
| tags: |
| - radiology |
| - clinical-nlp |
| - named-entity-recognition |
| - relation-extraction |
| - radgraph |
| - italian |
| configs: |
| - config_name: sample |
| data_files: |
| - split: train |
| path: data/radgraph_it_50.jsonl |
| --- |
| |
| # RadGraph-IT Dataset |
|
|
| ## Dataset summary |
|
|
| RadGraph-IT contains 2,850 Italian radiology reports with entity and relation |
| annotations in a DyGIE-compatible JSONL format. The corpus combines 2,300 |
| reports from [RadGraph-XL](https://aclanthology.org/2024.findings-acl.765/) |
| with 550 reports derived from |
| [RadGraph 1.0](https://physionet.org/content/radgraph/1.0.0/). |
|
|
| This Hugging Face repository provides a 50-report preview of the corpus used |
| for training. The complete dataset is available through |
| [Stanford AIMI on Redivis](https://stanford.redivis.com/datasets/d7b8-c3n9pedjr). |
| Please refer to the dataset page for the applicable license terms and access |
| requirements. |
|
|
| The full corpus includes 1,050 chest X-ray reports, 600 chest CT reports, 600 |
| abdomen/pelvis CT reports, and 600 brain MRI reports. |
|
|
| ## Translation and manual review |
|
|
| All 2,850 English reports were localized with the English-to-Italian pipeline |
| developed for the thesis *AI-Driven Keyword Extraction and Structuralization |
| of Medical Reports* and released in the |
| [`EIDOSLAB/radiomicslab-keyword-extraction`](https://github.com/EIDOSLAB/radiomicslab-keyword-extraction) |
| repository. |
|
|
| After translation and span remapping, the localized reports and projected |
| annotations were manually reviewed. The Italian text, entity boundaries, |
| entity labels, and relations were corrected where necessary. |
|
|
| ## Annotation schema |
|
|
| Each entity is a contiguous, inclusive token span assigned one of six labels: |
|
|
| - `Anatomy::definitely present` |
| - `Anatomy::uncertain` |
| - `Anatomy::definitely absent` |
| - `Observation::definitely present` |
| - `Observation::uncertain` |
| - `Observation::definitely absent` |
|
|
| Entities are connected by three directed relation types: |
|
|
| - `located_at`: an Observation and its anatomical site; |
| - `suggestive_of`: an Observation and another observation that it suggests; |
| - `modify`: two Observations or two Anatomy entities when one qualifies the |
| other. |
|
|
| ## Data format |
|
|
| The included [`data/radgraph_it_50.jsonl`](data/radgraph_it_50.jsonl) |
| uses the same representation consumed by the training stack. Each line is one |
| JSON object with: |
|
|
| - `dataset`: a provenance-aware RadGraph-IT source and modality label, such as |
| `radgraph-it-stanford-chest-x-ray`; |
| - `doc_key`: the numeric index from the original source subset; the pair |
| `(dataset, doc_key)` uniquely identifies and traces each report; |
| - `sentences`: a list containing the tokenized report; |
| - `ner`: entity tuples `[start, end, label]`, with inclusive token indices; |
| - `relations`: directed relation tuples |
| `[source_start, source_end, target_start, target_end, label]`, with inclusive |
| token indices. |
|
|
| The entire report is represented as one DyGIE "sentence" so relations can |
| connect entities anywhere in the report. |
|
|
| ## Sample distribution |
|
|
| The 50-report excerpt is distributed across the four imaging modalities as follows: |
|
|
| | Modality | Sample reports | |
| |---|---:| |
| | Chest X-ray | 18 | |
| | Chest CT | 11 | |
| | Abdomen/pelvis CT | 11 | |
| | Brain MRI | 10 | |
| | **Total** | **50** | |
|
|
