Multi-Granularity Aggregation Transformer For Light Field Image Super-Resolution

Hematoma expansion prediction is critical for the prognostic outcomes of patients with life-threatening intracerebral hemorrhage, which is time-consuming and replies on experts' experience. Though deep learning based approaches have greatly reduced labor costs in hematoma expansion prediction, there still exist two challenges: the high computational complexity brought by 3D-CNN-based models, and the poor performance based on single-modality medical imaging data. in this paper, we propose a bidirectional-guided semi-3D network, named BGSNet, to utilize multi-modality data for accurate hematoma expansion prediction with less computational complexity. Specifically, a hematoma attentive semi-3D (HAS) module is constructed to extract high-level 3D visual features with fewer parameters in 2D-CNN level. To explore the potential cross-modality associations, we propose a bidirectional-guided (BG) strategy for joint training on medical imaging data and clinical records simultaneously. Experimental results demonstrate the superior performance of the proposed framework, achieving an accuracy of 0.817, a recall of 0.679, and a specificity of 0.864 respectively. More importantly, the proposed framework is more light-weight compared to 3D networks, making it more feasible in clinical applications.