Fig. 4
From: Deep learning to identify stroke within 4.5 h using DWI and FLAIR in a prospective multicenter study
Overview of the proposed mRUNet architecture for stroke-onset time classification. Six preprocessed 3D multimodal images (DWI and FLAIR) are used as a six-channel input. A modified U-Net, consisting of contracting and expanding paths without copy-and-crop connections, generates a single 3D DWI–FLAIR mismatch image by focusing on global intensity differences. This mismatch image is then processed by a 3D ResNet-34, which extracts high-level semantic features through sequential residual blocks. After global average pooling and a fully connected layer with 300 units, a softmax output predicts whether stroke onset occurred within 4.5 h. 3D, three-dimensional; DWI, diffusion-weighted imaging; FLAIR, fluid-attenuated inversion recovery; mRUNet, multimodal Res-U-Net.
