SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

arXiv:2404.10156v2 Announce Type: replace
Abstract: The adoption of Vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding. While this paradigm shift has significantly enhanced 3D segmentation performance, state-of-the-art architectures require extremely large and complex architectures with large scale computing resources for training and deployment. Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles in …

arxiv cs.cv image medical segmentation transformer type