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WIMC 2026
Multimodal Deep Learning Model to Differentiate Viral from Bacterial Pneumonia Using CXR and Early Clinical Data
A mentee-presented multimodal model combining chest X-ray and early clinical data to distinguish viral from bacterial pneumonia.
- April 2026
- Warsaw, Poland
- Mentee presentation
Awarded
Session details
- WIMC
- Student/presenter prize — Mohammed Al-azzawi: 1st place, Infectious Diseases session
- No public assets
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Summary
What the session covered and why it mattered.
This mentee-presented work used chest X-ray data and early clinical variables to differentiate viral from bacterial pneumonia. The project focused on clinically timed multimodal learning, where the model uses information available near initial assessment rather than relying on late or retrospective signals.
Connects early clinical variables with imaging so pneumonia classification can be framed closer to the first decision point.
infectious diseasepneumoniachest X-raymultimodal AI
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