Recognition
1st place, Infectious Diseases session; reached Preliminary session
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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.
- 2026
- Warsaw, Poland
- Mentee presentation
Awarded
Session details
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- WIMC
- 1st place, Infectious Diseases session; reached Preliminary session
- No public assets
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Summary
What the session covered and why it mattered.
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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.
Session context
- WIMC 2026
- 2026
- Warsaw, Poland
- Mentee presentation
Outcome
Recognition, result, and the talk's core takeaway.
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Takeaway
Connects early clinical variables with imaging so pneumonia classification can be framed closer to the first decision point.
Topic map
infectious diseasepneumoniachest X-raymultimodal AI
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