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Datasets:
rishuKumar404
/
weasis-tabular-benchmark

Modalities:
Image
Tabular
Formats:
imagefolder
Languages:
English
Size:
< 1K
Tags:
medical-imaging
gui-automation
weasis
dicom
benchmark
end-to-end
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
Community
weasis-tabular-benchmark
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---
license: mit
task_categories:
- computer-vision
- gui-automation
- medical-imaging
language:
- en
tags:
- medical-imaging
- gui-automation
- weasis
- dicom
- benchmark
- end-to-end
- tabular
size_categories:
- 1K<n<10K
---

# Weasis Medical Imaging GUI Benchmark (Tabular Format)

## Dataset Description

This dataset contains 267 end-to-end GUI automation tasks for the Weasis medical imaging viewer in **tabular format**, where each row represents one complete task with all associated data.

### Dataset Summary

- **Total Tasks**: 267
- **Total Images**: 202
- **Format**: Tabular (each row = one task)
- **Application**: Weasis Medical Imaging Viewer
- **Resolution**: 1920x1080

### Data Structure

Each row contains:

| Column | Description | Type |
|--------|-------------|------|
| `serial_number` | Task number (1-267) | int64 |
| `instruction` | Natural language task description | string |
| `json_task` | Complete JSON data for the task | string |
| `image_sequence` | Screenshot sequence (→ separated) | string |
| `images` | All images for the task | List[Image] |
| `task_id` | Unique task identifier | string |
| `num_steps` | Number of steps in trajectory | int64 |
| `initial_image` | Starting image filename | string |
| `final_success` | Whether task completed successfully | bool |

### Usage

```python
from datasets import load_dataset
import json

# Load the dataset
dataset = load_dataset("YOUR_USERNAME/weasis-tabular-benchmark")

# Access a task (row)
task_row = dataset["train"][0]
print(f"Task {task_row['serial_number']}: {task_row['instruction']}")
print(f"Steps: {task_row['num_steps']}")
print(f"Image sequence: {task_row['image_sequence']}")

# Parse the JSON task data
task_json = json.loads(task_row['json_task'])
print(f"Trajectory steps: {len(task_json['trajectory'])}")

# Access images
for i, image in enumerate(task_row['images']):
    if image is not None:
        print(f"Image {i+1}: {image.size}")
```

### Task Examples

**Row 1: Basic DICOM Loading**
- Instruction: "Load CT abdomen series of Rishu, set a 1×2 layout, and invert contrast of one to compare them."
- Steps: 9
- Image sequence: "1.png → 2.png → Import DCM Slide CT Rishu.png → ..."
- Success: True

**Row 25: Measurement Task**
- Instruction: "Load chest X-ray of Rishu, use the Line tool to measure the heart width."
- Steps: 6
- Image sequence: "1.png → 2.png → ... → Line measurement.png"
- Success: True

### Action Types

- **CLICK**: Button clicks, menu selections, dialog interactions
- **SCROLL**: Image navigation, panning, scrolling
- **TEXT**: Text input, annotations, search fields
- **SEGMENT**: ROI drawing, measurement tools, annotation drawing
- **ZOOM**: Zoom in/out operations
- **COMPLETE**: Task completion, saving, exporting

### Advantages of Tabular Format

1. **Easy Analysis**: Each task is one row
2. **Quick Filtering**: Filter by instruction type, success rate, etc.
3. **Image Access**: All images for a task in one place
4. **JSON Parsing**: Full task data available when needed
5. **CSV Export**: Can be opened in Excel/Google Sheets

### Citation

```bibtex
@dataset{weasis_tabular_benchmark_2024,
  title={Weasis Medical Imaging GUI Benchmark (Tabular Format)},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/datasets/rishuKumar404/weasis-tabular-benchmark}
}
```

### License

MIT License