| --- |
| license: mit |
| task_categories: |
| - computer-vision |
| - other |
| size_categories: |
| - 1K<n<10K |
| language: |
| - en |
| tags: |
| - medical-imaging |
| - gui-automation |
| - computer-vision |
| - benchmark |
| - weasis |
| - dicom |
| pretty_name: Weasis Medical Imaging GUI Benchmark |
| --- |
| |
| # 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("rishuKumar404/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 |
|
|
| - **Easy Analysis**: Each task is one row |
| - **Quick Filtering**: Filter by instruction type, success rate, etc. |
| - **Image Access**: All images for a task in one place |
| - **JSON Parsing**: Full task data available when needed |
| - **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={Rishu Kumar}, |
| year={2024}, |
| url={https://huggingface.co/datasets/rishuKumar404/weasis-tabular-benchmark} |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT License |
|
|
