---
pretty_name: CORTEX
title: "CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs"
tags:
  - medical-imaging
  - chest-ct
  - radiology
  - multimodal
  - visual-question-answering
  - structured-reasoning
  - trustworthy-ai
task_categories:
  - visual-question-answering
  - image-text-to-text
  - text-generation
language:
  - en
modality:
  - text
  - image
size_categories:
  - 10K<n<100K
source_datasets:
  - CT-RATE
---

# CORTEX

## A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs

CORTEX (Clinically Organized Reasoning and sTructured EXplanation) is a structured reasoning benchmark for multimodal large language models working with 3D chest CT. It converts chest CT question-answering and report-generation tasks into auditable, radiologist-inspired reasoning traces.

This Hugging Face repository is the **data release** for the paper:

> Hashmat Shadab Malik, Anees Ur Rehman Hashmi, Numan Saeed, Muzammal Naseer, Salman Khan, and Christoph Lippert. “CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs.”

- Paper: [arXiv:2606.27264](https://arxiv.org/abs/2606.27264)
- Venue: **SAFER 2026**, **MICCAI 2026**
- Github repo: [CORTEX GitHub repository](https://github.com/aneesurhashmi/CORTEX)

## Dataset summary

The release contains three task families. CORTEX was constructed from the validation split of [CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE).

| Configuration | File | Examples in this release | Description |
| --- | --- | ---: | --- |
| `closedended` | `data/closedended.json` | 8,910 | Multiple-choice chest CT VQA with structured reasoning |
| `openended` | `data/openended.json` | 64,211 | Open-ended chest CT VQA with structured reasoning |
| `report` | `data/report.json` | 3,039 | Chest CT report-generation examples with structured reasoning |
| **Total** | — | **76,160** | — |


## Dataset structure

Each configuration is stored as a JSON array. Records contain:

| Field | Type | Description |
| --- | --- | --- |
| `id` | string | Sample identifier |
| `ct_rate_id` | string | Identifier inherited from the source VQA in CT-RATE |
| `type` | string | `closedended`, `openended`, or `report` |
| `image` | string | Referenced CT-RATE volume filename |
| `conversations` | list | Ordered human/model messages |

The `conversations` field follows the common conversational format used by multimodal instruction datasets. Model messages contain a structured reasoning trace with four stages:

1. `<task>` — task and clinical-context understanding
2. `<observation>` — visual assessment organized by anatomy
3. `<reason>` — option or hypothesis evaluation
4. `<answer>` — final answer and rationale, or report synthesis


## Prompts

The task-specific prompts used to generated the reasoning traces in this dataset are included for reproducibility:

```text
prompts/
├── generation/
│   ├── closedended.md
│   ├── openended.md
│   └── report.md
└── scoring/
    ├── closedended.md
    ├── openended.md
    └── report.md
```

Generation prompts produce the four-stage CORTEX reasoning traces. Scoring prompts apply the corresponding automated evaluation. 

## Download


```python
from huggingface_hub import snapshot_download

local_path = snapshot_download(
    repo_id="aneesurhashmi/cortex",
    repo_type="dataset",
    local_dir="./cortex",
)

print(f"Downloaded to: {local_path}")
```

## Intended use

CORTEX is intended for research on multimodal clinical question answering, radiology report generation, structured diagnostic reasoning, and stage-level evaluation of reasoning traces.

This dataset is not a clinical decision-support system. It must not be used as a substitute for qualified radiologist interpretation or patient care. Users should review the source dataset’s license, privacy documentation, data-use terms, and institutional requirements before downloading, using, or redistributing the data.

## Data provenance

CORTEX was constructed from the validation split of [CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE), a chest CT question-answering dataset. The reasoning traces were generated with task-specific prompts and selected using expert-in-the-loop + rubric-based verification. Please read the paper for the full generation and validation methodology.

## Limitations

- The benchmark is limited to chest CT and English-language text.
- Reasoning traces are structured model-generated annotations and should not be treated as independent expert reports.
- The data snapshot and paper totals differ as documented above.
- Dataset access, licensing, de-identification details, and CT-volume hosting should be confirmed from the final release configuration.

## Citation

```bibtex
@article{malik2026cortex,
  title   = {CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs},
  author  = {Malik, Hashmat Shadab and Hashmi, Anees Ur Rehman and Saeed, Numan and Naseer, Muzammal and Khan, Salman and Lippert, Christoph},
  journal = {arXiv preprint arXiv:2606.27264},
  year    = {2026}
}
```