Fig. 1: Comprehensive comparison of medical image segmentation models on ISIC2016 dataset.
From: GH-UNet: group-wise hybrid convolution-VIT for robust medical image segmentation
This figure presents a comprehensive comparison of different segmentation models in terms of Dice score, IoU score, number of parameters, and FLOPs on the ISIC2016 dataset. GH-UNet demonstrates superior segmentation performance while requiring fewer parameters and lower computational cost compared to state-of-the-art models such as H2Former and TransUNet.
