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When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation. (arXiv:2208.06449v1 [eess.IV])
cs.CV updates on arXiv.org arxiv.org
Due to the lack of quality annotation in medical imaging community,
semi-supervised learning methods are highly valued in image semantic
segmentation tasks. In this paper, an advanced consistency-aware
pseudo-label-based self-ensembling approach is presented to fully utilize the
power of Vision Transformer(ViT) and Convolutional Neural Network(CNN) in
semi-supervised learning. Our proposed framework consists of a feature-learning
module which is enhanced by ViT and CNN mutually, and a guidance module which
is robust for consistency-aware purposes. The pseudo labels are inferred and …
arxiv cnn image learning medical segmentation semantic semi-supervised semi-supervised learning supervised learning