A Multimodal Deep Learning Framework for Edema Classification Using HCT and Clinical Data

1. 1Collect and preprocess HCT images and corresponding clinical metadata
2. 2Design a multimodal deep learning framework that can effectively fuse heterogeneous data sources
3. 3Implement AttentionMixer to learn spatial and contextual features from HCT and clinical data
4. 4Evaluate the performance of AttentionMixer on edema classification tasks

This research benefits data scientists and AI engineers working in healthcare, as it provides a novel approach to combining medical imaging and clinical data for improved diagnosis accuracy

💡 Fusing structural HCT images with clinical metadata can improve edema classification accuracy

💡 Multimodal deep learning for edema classification: combining HCT & clinical data with AttentionMixer