MCADS, a decoder with depth-to-space upsampling and residual linear attention, improves biomarker segmentation by about three to four percent IoU over previous methods on four public datasets.
Ma-unet: An improved ver- sion of unet based on multi-scale and attention mechanism for medical image segmentation
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Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention
MCADS, a decoder with depth-to-space upsampling and residual linear attention, improves biomarker segmentation by about three to four percent IoU over previous methods on four public datasets.