# FAD-ViT

Medical transfer learning has long inherited spatial-attention backbones pretrained on natural images, yet layout priors do not survive shifts across scanners and modalities. FAD-ViT moves global modeling from spatial tokens to channel tokens and lets depthwise convolution handle local spatial extraction alone, strictly decoupling the two axes. Across 23 classification, segmentation and low-data tasks, the two axes are nearly tied from scratch, while identical ImageNet pretraining yields up to roughly three times the transfer gain on the feature axis, raising ultrasound segmentation Dice from 53.1 to 77.7 at about a quarter of the comparison's parameters.

Medical transfer learning has long inherited spatial-attention backbones pretrained on natural images, yet layout priors do not survive shifts across scanners and modalities. FAD-ViT moves global modeling from spatial tokens to channel tokens and lets depthwise convolution handle local spatial extraction alone, strictly decoupling the two axes. Across 23 classification, segmentation and low-data tasks, the two axes are nearly tied from scratch, while identical ImageNet pretraining yields up to roughly three times the transfer gain on the feature axis, raising ultrasound segmentation Dice from 53.1 to 77.7 at about a quarter of the comparison's parameters.