A Multimodal Deep Learning Framework for Edema Classification Using HCT and Clinical Data
We propose AttentionMixer, a unified deep learning framework for multimodal detection of brain edema that combines structural head CT (HCT) with routine clinical metadata. While HCT provides rich spatial information, clinical variables such as age, laboratory values, and scan timing capture complementary context that might be ignored or naively concatenated. AttentionMixer is designed to fuse these heterogeneous sources in a principled and efficient manner. HCT volumes are first encoded using a self-supervised Vision Transformer Autoencoder (ViT-AE++), without requiring large labeled datasets. Clinical metadata are mapped into the same feature space and used as keys and values in a cross-...
| Authors | Aram Ansary Ogholbake, Hannah Choi, Spencer Brandenburg et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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