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.
Session details
2026 / Warsaw, Poland
WIMC
1st place, Infectious Diseases session; reached Preliminary session
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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.
Session context
WIMC 2026
2026 / Warsaw, Poland
Mentee presentation
Outcome
Recognition, result, and the talk's core takeaway.
Result
1st place, Infectious Diseases session; reached Preliminary session
Recognition captured from the conference program and retained on the canonical talk page.
Takeaway
Connects early clinical variables with imaging so pneumonia classification can be framed closer to the first decision point.
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Contact
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