Blame for README.md

---
license: cc-by-nc-sa-4.0
language:
- en
pretty_name: CT-RATE-Refined
tags:
- medical
- radiology
- chest-ct
- ct-rate
- label-cleaning
size_categories:
- 10K<n<100K
configs:
- config_name: majority_vote
  default: true
  data_files:
  - split: train
    path: labels/volume_level/train_predicted_labels.csv
  - split: validation
    path: labels/volume_level/valid_predicted_labels.csv
- config_name: report_level
  data_files:
  - split: majority_vote
    path: labels/report_level/majority_vote.csv
  - split: positive_votes
    path: labels/report_level/positive_votes.csv
  - split: gpt_5.4
    path: labels/report_level/gpt-5.4.csv
  - split: gpt_5.4_mini
    path: labels/report_level/gpt-5.4-mini.csv
  - split: deepseek_v3.2
    path: labels/report_level/deepseek-v3.2.csv
- config_name: confirmatory_reference
  data_files:
  - split: reference_standard
    path: reference/confirmatory/reference_standard.csv
  - split: preconsensus_reader_1
    path: reference/confirmatory/preconsensus_reader_1.csv
  - split: preconsensus_reader_2
    path: reference/confirmatory/preconsensus_reader_2.csv
  - split: design
    path: reference/confirmatory/design.csv
---

# CT-RATE-Refined

Refined report-aligned abnormality labels for the [CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE) chest CT dataset, generated by multi-LLM majority voting (GPT-5.4, GPT-5.4 mini, and DeepSeek-V3.2), together with the model-specific outputs and the radiologist reference annotations used for validation.

This dataset accompanies the paper *"Large Language Model–Assisted Cleaning of Report-Derived Labels in a Large-Scale Chest CT Dataset"*, accepted for publication in the **Journal of Medical Systems**. Methods and results are described in the paper ([arXiv:2606.22382](https://arxiv.org/abs/2606.22382)). Code: <https://github.com/yamagishi0824/ct-rate-llm-label-cleaning>.

> **Note.** All labels were derived from report text. They reflect report alignment, not image-level truth.

## Relationship to CT-RATE

This repository contains **labels only**. CT volumes and radiology reports are available from [ibrahimhamamci/CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE) (access must be requested there). All files are keyed by the CT-RATE `VolumeName` (e.g., `train_1_a_1.nii.gz`).

- `labels/volume_level/{train,valid}_predicted_labels.csv` have the same rows, row order, and columns as CT-RATE's `dataset/multi_abnormality_labels/{train,valid}_predicted_labels.csv` and can be used as drop-in replacements.
- Labels were assigned per unique report (24,446 reports after deduplication on Findings and Impressions text) and propagated to every volume sharing that report. `mapping/volume_to_report.csv` gives this correspondence.

The 18 label columns follow CT-RATE: Medical material, Arterial wall calcification, Cardiomegaly, Pericardial effusion, Coronary artery wall calcification, Hiatal hernia, Lymphadenopathy, Emphysema, Atelectasis, Lung nodule, Lung opacity, Pulmonary fibrotic sequela, Pleural effusion, Mosaic attenuation pattern, Peribronchial thickening, Consolidation, Bronchiectasis, Interlobular septal thickening.

## Files

### `labels/` — full dataset

| File | Description |
|---|---|
| `volume_level/train_predicted_labels.csv`, `volume_level/valid_predicted_labels.csv` | Majority-vote labels per CT volume, in CT-RATE format (1 = present, 0 = absent). |
| `report_level/majority_vote.csv` | Majority-vote labels per unique report. `VolumeName` is the representative volume of the report; `n_models_available` is the number of models with valid output. A label is assigned when at least two models selected the same class and is empty otherwise (11 reports). |
| `report_level/positive_votes.csv` | Number of models assigning 1 to each label (0–`n_models_available`), as an indicator of intermodel agreement. |
| `report_level/{gpt-5.4,gpt-5.4-mini,deepseek-v3.2}.csv` | Labels from each model. `extraction_failed` = True for reports without valid output (GPT-5.4: 12; GPT-5.4 mini: 10; DeepSeek-V3.2: 0); their labels are empty. |

### `mapping/`

| File | Description |
|---|---|
| `volume_to_report.csv` | `VolumeName`, `split`, and `report_VolumeName` (the representative volume used in `report_level/`). |

### `reference/development/` — development-phase radiologist annotations

| File | Description |
|---|---|
| `random_set.csv` | Development reference set: 100 randomly sampled reports × 18 labels, binary annotations by two readers (`reader_1`, `reader_2`). |
| `discordance_general.csv` | General discordance review set: CT-RATE and GPT-5.4 labels and the label supported by each reader (`gpt-5.4`, `ct-rate`, or `both`). |
| `discordance_lymphadenopathy.csv` | Targeted lymphadenopathy discordance review set; same columns. |

### `reference/confirmatory/` — confirmatory reference set (250 reports, validation split)

| File | Description |
|---|---|
| `design.csv` | Sampled reports with reading order, stratum, stratum size (`stratum_N`), sample size (`stratum_n`), and design weight. |
| `preconsensus_reader_{1,2}.csv` | Independent four-level annotations (`present`, `uncertain`, `negated`, `not_mentioned`) before consensus. `temporality` (`current`, `prior_only`, or `resolved`) is filled only for `present` and `uncertain`. |
| `reference_standard.csv` | Both readers' annotations, the consensus four-level label, `source` (`agreed` or `adjudicated`), and the binary label under Rule A (`present` and `uncertain` = 1). |
| `llm_runs/{model}_run{1,2,3}.csv` | Repeated binary runs of each model on the confirmatory set. |
| `llm_runs/{model}_4level_run1.csv` | Four-level run of each model on the confirmatory set. |

## License

CC BY-NC-SA 4.0, following the license of CT-RATE.

## Citation

If you use these labels, please cite our paper and the original CT-RATE paper.

The paper has been accepted for publication in the *Journal of Medical Systems*. Until the journal version is available, please cite the arXiv preprint below.

```bibtex
@article{yamagishi2026llmlabelcleaning,
  title   = {Large Language Model-Assisted Cleaning of Report-Derived Labels in a Large-Scale Chest CT Dataset},
  author  = {Yamagishi, Yosuke and Takamatsu, Atsushi and Sato, Mototsugu and Kikuchi, Tomohiro and Hanaoka, Shouhei and Yoshikawa, Takeharu and Abe, Osamu},
  journal = {arXiv preprint arXiv:2606.22382},
  year    = {2026},
  doi     = {10.48550/arXiv.2606.22382}
}

@article{hamamci2026ctrate,
  title   = {Generalist foundation models from a multimodal dataset for 3D computed tomography},
  author  = {Hamamci, Ibrahim Ethem and Er, Sezgin and Wang, Chenyu and Almas, Furkan and Simsek, Ayse Gulnihan and Esirgun, Sevval Nil and others},
  journal = {Nature Biomedical Engineering},
  volume  = {10},
  number  = {8},
  pages   = {1610--1628},
  year    = {2026},
  doi     = {10.1038/s41551-025-01599-y}
}
```