Prompts Engineering

AI-assisted Lung Cancer Screening System Development Guide

Prompt Edit here before copy/try — source content is unchanged

1. Objective: Develop an AI-assisted lung cancer screening system that takes CT images as input and outputs cancer suspicion ratings (no suspicion, probably benign, suspicious, highly suspicious) along with corresponding regions of interest.
2. System Architecture:
   - Utilize 13 models working in coordination
   - Steps: Segment lungs -> Overall assessment -> Locate 3 suspicious regions -> Assign suspicion rating to each region
   - Deploy on Google Kubernetes Engine (GKE)
3. Model Improvements:
   - Increase training data
   - Incorporate self-attention mechanism
4. User Interface:
   - Output images directly associated with CT study
   - No changes required to user's workstation
   - Radiologists only need to review a small set of additional images
5. Evaluation Method:
   - Conduct randomized reader studies to assess system utility
   - Metrics: Sensitivity, specificity, Area Under ROC Curve (AUC)
   - Compare doctor performance with and without AI assistance
6. Output Format:
   - Suspicion rating
   - Localization of regions of interest
   - Visualization: Use circles to mark suspicious lesions, squares to show sagittal view rendering

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Usage & requirements

Follow the guide to develop a lung cancer screening AI system, deploy on GKE and evaluate system performance

Tools: google kubernetes engine machine learning models
Variables: ct_images suspicion_rating regions_of_interest
Source: Google Research Blog ↗

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