HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for Ultrasound Image Segmentation

• URL: http://arxiv.org/abs/2407.04203v2
• Date: Fri, 16 Aug 2024 08:27:14 GMT
• Title: HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for Ultrasound Image Segmentation
• Authors: Renqi Chen, Xinzhe Zheng, Haoyang Su, Kehan Wu,
• Abstract summary: We introduce a hybrid constraint-driven semi-supervised Transformer-NAS (HCS-TNAS) for ultrasound segmentation. HCS-TNAS includes an Efficient NAS-ViT module for multi-scale token search before ViT's attention calculation, effectively capturing contextual and local information with lower computational costs. Experiments on public datasets show that HCS-TNAS achieves state-of-the-art performance, pushing the limit of ultrasound segmentation.
• Score: 0.34089646689382486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise ultrasound segmentation is vital for clinicians to provide comprehensive diagnoses. However, developing a model that accurately segments ultrasound images is challenging due to the images' low quality and the scarcity of extensive labeled data. This results in two main solutions: (1) optimizing multi-scale feature representations, and (2) increasing resistance to data dependency. The first approach necessitates an advanced network architecture, but a handcrafted network is knowledge-intensive and often yields limited improvement. In contrast, neural architecture search (NAS) can more easily attain optimal performance, albeit with significant computational costs. Regarding the second issue, semi-supervised learning (SSL) is an established method, but combining it with complex NAS faces the risk of overfitting to a few labeled samples without extra constraints. Therefore, we introduce a hybrid constraint-driven semi-supervised Transformer-NAS (HCS-TNAS), balancing both solutions for segmentation. HCS-TNAS includes an Efficient NAS-ViT module for multi-scale token search before ViT's attention calculation, effectively capturing contextual and local information with lower computational costs, and a hybrid SSL framework that adds network independence and contrastive learning to the optimization for solving data dependency. By further developing a stage-wise optimization strategy, a rational network structure is identified. Experiments on public datasets show that HCS-TNAS achieves state-of-the-art performance, pushing the limit of ultrasound segmentation.

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