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rishuKumar404/weasis-optimized-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… See the full description on the dataset page: https://huggingface.co/datasets/rishuKumar404/weasis-optimized-benchmark.

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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:

ColumnDescriptionType
serial_numberTask number (1-267)int64
instructionNatural language task descriptionstring
json_taskComplete JSON data for the taskstring
image_sequenceScreenshot sequence (→ separated)string
imagesAll images for the taskList[Image]
task_idUnique task identifierstring
num_stepsNumber of steps in trajectoryint64
initial_imageStarting image filenamestring
final_successWhether task completed successfullybool

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