HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for Ultrasound Image Segmentation
- URL: http://arxiv.org/abs/2407.04203v1
- Date: Fri, 5 Jul 2024 01:02:12 GMT
- Title: HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for Ultrasound Image Segmentation
- Authors: Renqi Chen,
- Abstract summary: We propose HCS-TNAS, a novel neural architecture search (NAS) method that automatically designs the network.
For the first concern, we employ multi-level searching encompassing cellular, layer, and module levels.
For the second concern, we propose a hybrid constraint-driven semi-supervised learning method that considers additional network independence.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate ultrasound segmentation is pursued because it aids clinicians in achieving a comprehensive diagnosis. Due to the presence of low image quality and high costs associated with annotation, two primary concerns arise: (1) enhancing the understanding of multi-scale features, and (2) improving the resistance to data dependency. To mitigate these concerns, we propose HCS-TNAS, a novel neural architecture search (NAS) method that automatically designs the network. For the first concern, we employ multi-level searching encompassing cellular, layer, and module levels. Specifically, we design an Efficient NAS-ViT module that searches for multi-scale tokens in the vision Transformer (ViT) to capture context and local information, rather than relying solely on simple combinations of operations. For the second concern, we propose a hybrid constraint-driven semi-supervised learning method that considers additional network independence and incorporates contrastive loss in a NAS formulation. By further developing a stage-wise optimization strategy, a rational network structure can be identified. Extensive experiments on three publicly available ultrasound image datasets demonstrate that HCS-TNAS effectively improves segmentation accuracy and outperforms state-of-the-art methods.
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