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?
