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README.md

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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