Rethinking U-net Skip Connections for Biomedical Image Segmentation

The U-net architecture has significantly impacted deep learning-based segmentation of medical images. Through the integration of long-range skip connections, it facilitated the preservation of high-resolution features. Out-of-distribution data can, however, substantially impede the performance of neural networks. Previous works showed that the trained network layers differ in their susceptibility to this domain shift, e.g., shallow layers are more affected than deeper layers. In this work, we investigate the implications of this observation of layer sensitivity to domain shifts of U-net-style …

architecture biomedical cs.cv data deep learning distribution domain eess.iv features image images integration medical network networks neural networks performance preservation segmentation through