# Lt Vit Cxr Eval

> Evaluates a vision transformer's ability to perform multi-label classification on chest X-ray images. It probes the model's capacity to detect multiple pathologies simultaneously and model inter-label dependencies using learnable label tokens. Use when the user wants to benchmark on NIH-CXR14, CheXpert-5, CheXpert-13, or asks about evaluating this task. Reports AUC (%).

- Skill: `qhjqhj00/lt-vit-cxr-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/lt-vit-cxr-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/lt-vit-cxr-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/lt-vit-cxr-eval

---


# lt-vit-cxr-eval

> LT-ViT: A Vision Transformer for multi-label Chest X-ray classification — Marikkar et al. (2023) (arXiv:2311.07263, 2023)

## What this evaluates

Evaluates a vision transformer's ability to perform multi-label classification on chest X-ray images. It probes the model's capacity to detect multiple pathologies simultaneously and model inter-label dependencies using learnable label tokens.

## Datasets

- **NIH-CXR14** — total ?; splits: test (-1)
- **CheXpert-5** — total ?; splits: test (-1)
- **CheXpert-13** — total ?; splits: test (-1)

## Metrics

- `AUC (%)` **(primary)** — range: percent
  - Area Under the Receiver Operating Characteristic Curve computed for multi-label classification. Typically calculated per pathology and averaged (macro-average) across all labels.

## Input / output format

**Input**: Chest X-ray radiograph images resized to 224×224 pixels.

**Output**: Multi-label probability scores or binary predictions for each of the 5 or 13 specified pathologies.

## Scoring recipe

```python
def compute_auc(predictions, gold):
    # predictions: (N, C) float array of probabilities
    # gold: (N, C) binary array
    aucs = []
    for c in range(predictions.shape[1]):
        auc = roc_auc_score(gold[:, c], predictions[:, c])
        aucs.append(auc)
    return np.mean(aucs) * 100
```

## Common pitfalls

- AUC is reported as a percentage in tables, but the underlying metric ranges from 0 to 1.
- Multi-label AUC averaging strategy (macro vs. micro) is not explicitly specified in the text.
- Image resolution is fixed at 224×224 for fair comparison; deviating from this changes performance significantly.

## Evidence (verbatim from paper)

> Table 1 presents the comparison between the proposed method and previously established multi-label classification methods using transformers.

*Table 1: Comparison of LT-ViT vs. existing benchmarks pre-trained on NIH-CXR14.*

| Method | Model | Params | Img.res | AUC (%) | | |
| --- | --- | --- | --- | --- | --- | --- |
| | | | | NIH-14 | CheX-05 | CheX-13 |

## Citation

```bibtex
@misc{marikkar2023ltvit,
  title={LT-ViT: A Vision Transformer for multi-label Chest X-ray classification},
  author={Marikkar et al. (2023)},
  year={2023},
  note={arXiv:2311.07263}
}
```

- arXiv: 2311.07263