README.md

metadata

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 with 550 reports derived from RadGraph 1.0.

This repository provides a 50-report excerpt of the corpus used for training. The complete RadGraph-IT corpus is not currently available for publication. 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 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 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