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--- |
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license: mit |
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task_categories: |
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- computer-vision |
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- gui-automation |
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- medical-imaging |
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language: |
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- en |
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tags: |
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- medical-imaging |
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- gui-automation |
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- weasis |
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- dicom |
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- benchmark |
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- end-to-end |
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- tabular |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Weasis Medical Imaging GUI Benchmark (Tabular Format) |
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## Dataset Description |
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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. |
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### Dataset Summary |
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- **Total Tasks**: 267 |
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- **Total Images**: 202 |
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- **Format**: Tabular (each row = one task) |
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- **Application**: Weasis Medical Imaging Viewer |
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- **Resolution**: 1920x1080 |
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### Data Structure |
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Each row contains: |
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| Column | Description | Type | |
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|--------|-------------|------| |
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| `serial_number` | Task number (1-267) | int64 | |
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| `instruction` | Natural language task description | string | |
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| `json_task` | Complete JSON data for the task | string | |
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| `image_sequence` | Screenshot sequence (→ separated) | string | |
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| `images` | All images for the task | List[Image] | |
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| `task_id` | Unique task identifier | string | |
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| `num_steps` | Number of steps in trajectory | int64 | |
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| `initial_image` | Starting image filename | string | |
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| `final_success` | Whether task completed successfully | bool | |
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### Usage |
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```python |
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from datasets import load_dataset |
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import json |
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# Load the dataset |
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dataset = load_dataset("YOUR_USERNAME/weasis-tabular-benchmark") |
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# Access a task (row) |
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task_row = dataset["train"][0] |
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print(f"Task {task_row['serial_number']}: {task_row['instruction']}") |
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print(f"Steps: {task_row['num_steps']}") |
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print(f"Image sequence: {task_row['image_sequence']}") |
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# Parse the JSON task data |
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task_json = json.loads(task_row['json_task']) |
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print(f"Trajectory steps: {len(task_json['trajectory'])}") |
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# Access images |
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for i, image in enumerate(task_row['images']): |
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if image is not None: |
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print(f"Image {i+1}: {image.size}") |
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``` |
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### Task Examples |
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**Row 1: Basic DICOM Loading** |
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- Instruction: "Load CT abdomen series of Rishu, set a 1×2 layout, and invert contrast of one to compare them." |
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- Steps: 9 |
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- Image sequence: "1.png → 2.png → Import DCM Slide CT Rishu.png → ..." |
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- Success: True |
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**Row 25: Measurement Task** |
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- Instruction: "Load chest X-ray of Rishu, use the Line tool to measure the heart width." |
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- Steps: 6 |
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- Image sequence: "1.png → 2.png → ... → Line measurement.png" |
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- Success: True |
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### Action Types |
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- **CLICK**: Button clicks, menu selections, dialog interactions |
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- **SCROLL**: Image navigation, panning, scrolling |
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- **TEXT**: Text input, annotations, search fields |
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- **SEGMENT**: ROI drawing, measurement tools, annotation drawing |
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- **ZOOM**: Zoom in/out operations |
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- **COMPLETE**: Task completion, saving, exporting |
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### Advantages of Tabular Format |
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1. **Easy Analysis**: Each task is one row |
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2. **Quick Filtering**: Filter by instruction type, success rate, etc. |
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3. **Image Access**: All images for a task in one place |
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4. **JSON Parsing**: Full task data available when needed |
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5. **CSV Export**: Can be opened in Excel/Google Sheets |
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### Citation |
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```bibtex |
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@dataset{weasis_tabular_benchmark_2024, |
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title={Weasis Medical Imaging GUI Benchmark (Tabular Format)}, |
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author={Your Name}, |
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year={2024}, |
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url={https://huggingface.co/datasets/rishuKumar404/weasis-tabular-benchmark} |
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} |
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``` |
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### License |
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MIT License |
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