README.md

metadata

license: cc-by-nc-sa-4.0
task_categories:
  - visual-question-answering
  - video-text-to-text
language:
  - en
tags:
  - medical
  - chest-ct
  - radiology
  - ct-rate
  - dpo
size_categories:
  - 10K<n<100K
pretty_name: CT-RATE-AB
configs:
  - config_name: AB
    data_files:
      - split: train
        path: viewer/train.parquet
      - split: valid
        path: viewer/valid.parquet
  - config_name: AB_DPO
    data_files:
      - split: train
        path: viewer/dpo.parquet

CT-RATE-AB

Vision-language annotations for chest CT abnormality reporting, derived from CT-RATE and formatted in the LLaVA conversation schema. Includes both an SFT split (train / valid) and a DPO preference set.

⚠️ Research use only. Not a medical device. Do not use for clinical decisions.

Splits

Subset	# Samples	Purpose
train	46,709	SFT training
valid	3,039	Validation
DPO	46,709	DPO preference fine-tuning

The DPO entries share id values with the SFT train set (the chosen response equals the SFT gpt value); train and valid splits inherit the official CT-RATE partition (no patient overlap).

⚠️ Two file formats in this repo

This repository ships two parallel views of the same data:

Purpose	Files	Format
Training (use these)	CT-RATE-AB-train.json, CT-RATE-AB-valid.json, CT-RATE-AB-DPO.json	Raw LLaVA-format JSON
Dataset Viewer only	viewer/train.parquet, viewer/valid.parquet, viewer/dpo.parquet	Simplified parquet

For training, always load the JSON files. They preserve the full LLaVA conversation schema (videos, conversations with <video> token, etc.) expected by standard multimodal training pipelines.

The parquet files in viewer/ exist only so the Hugging Face Dataset Viewer shows the report text cleanly — they strip out the videos filename column (no videos are hosted here) and the boilerplate human prompt, leaving just id and the report. Do not use parquet for training. The two views are guaranteed identical in content; they differ only in field layout.

Data format (the JSON files)

Standard LLaVA video-conversation schema.

CT-RATE-AB-train.json / CT-RATE-AB-valid.json (SFT):

{
  "id": "train_1_a_1",
  "videos": ["train_1_a_1.mp4"],
  "conversations": [
    {"from": "human", "value": "<video>\nGenerate a report for this patient."},
    {"from": "gpt",   "value": "Lung: ...\n\nMediastinum: ...\n\n..."}
  ]
}

CT-RATE-AB-DPO.json (DPO preference pairs):

{
  "id": "train_5251_a_1",
  "videos": ["train_5251_a_1.mp4"],
  "conversations": [
    {"from": "human", "value": "<video>\nGenerate a report for this patient."}
  ],
  "chosen":   {"from": "gpt", "value": "Lung: ... atelectatic changes ..."},
  "rejected": {"from": "gpt", "value": "Lung: ... ground-glass opacities ..."}
}

The assistant response is a structured report organized by anatomy: Lung, Trachea and Bronchie, Mediastinum, Bone, Abdomen, Others. The human prompt is identical across all samples: <video>\nGenerate a report for this patient.

Obtaining the CT volumes

This repository contains only annotations. To obtain the corresponding CT data, request access to CT-RATE at https://huggingface.co/datasets/ibrahimhamamci/CT-RATE. The id field in each entry maps directly to the CT-RATE volume identifier (and to the mp4 filename listed in videos, if you render videos locally).

License

Released under CC BY-NC-SA 4.0, consistent with the upstream CT-RATE license.