arxiv
PublishedJuly 2, 2026 at 4:00 AM
Explainability in mulimodal deep transformation models for stroke outcome prediction
Publisher summary· verbatim
arXiv:2504.06299v2 Announce Type: replace-cross Abstract: Multimodal prediction models based on imaging and clinical data are increasingly used for clinical decision support, yet their interpretability remains limited. We present multimodal Deep Transformation Models (DTMs) combining statistical app
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response9harxivLearning Transferable Latent User Preferences for Human-Aligned Decision Making9harxivRepair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents9harxivIndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD9hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗