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Transformer, as a new generation of neural architecture, has demonstrated remarkable performance in natural language processing and computer vision. However, existing vision Transformers struggle to learn with limited medical data and are unable to generalize on diverse medical image tasks. To tackle these challenges, we present UTNetV2 as a data-scalable Transformer towards generalizable medical image segmentation. The key designs incorporate desirable inductive bias, hierarchical modelingarXiv:2203.00131v3 fatcat:dmuh4yga4rahzjjdy4ttg7eei4