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class MultimodalFusion:
"""
Combines insights from image analysis and text analysis
to provide comprehensive medical assessment
"""
def __init__(self):
pass
def fuse_insights(self, image_results, text_results):
"""
Fuse insights from image and text analysis
Args:
image_results (dict): Results from image analysis
text_results (dict): Results from text analysis
Returns:
dict: Combined insights with recommendation
"""
# In a real implementation, this would use more sophisticated fusion techniques
combined_insights = {
"Image findings": image_results,
"Text findings": text_results,
}
# Simple fusion logic
confidence_scores = [
value for key, value in image_results.items() if isinstance(value, float)
]
avg_confidence = (
sum(confidence_scores) / len(confidence_scores) if confidence_scores else 0
)
# Determine if any abnormalities are detected in image
image_abnormal = any(
key != "No findings" and value > 0.5
for key, value in image_results.items()
if isinstance(value, float)
)
# Check if text analysis found concerning elements
text_concerning = text_results.get("Sentiment") == "Concerning"
# Generate recommendation
if image_abnormal and text_concerning:
recommendation = "High priority: Both image and text indicate abnormalities"
elif image_abnormal:
recommendation = "Medium priority: Image shows potential abnormalities"
elif text_concerning:
recommendation = "Medium priority: Text report indicates concerns"
else:
recommendation = "Low priority: No significant findings detected"
combined_insights["Recommendation"] = recommendation
combined_insights["Confidence"] = f"{avg_confidence:.2f}"
return combined_insights
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