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I am just getting into medical image segmentation and have been able to understand the state-of-the-art architectures, like Double UNet, UNet++, and Multiresunet.

What I haven't understood yet: Why are these approaches better for medical segmentation than, for example, HRNet-OCR, which currently tops the rankings of the Cityscapes dataset, and vice versa?

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  • $\begingroup$ Why do you think that UNet++ is better than HRNet-OCR? Can you please provide a reference that supports this conclusion? It may also be a good idea to include links to the papers that introduced all these models and to some site or whatever that supports this claim "HRNet-OCR, which currently tops the rankings of the Cityscapes dataset". $\endgroup$
    – nbro
    Feb 1 at 17:48

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