arXiv:2605.12567v2 Announce Type: replace-cross Abstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit noise assumptions whose validity diminishes under composite noise conditions. Learning-based methods are usually pretrained in a limited image domain using a labeled dataset, which implies inevitable domain shift in complex in vivo environments. This study proposes a Pyramid Self-Contrastive Learning (PSCL) framework for test-time ultrasound image denoising without pretraining. Given multipl
The proliferation of AI in medical imaging necessitates robust denoising techniques to improve diagnostic accuracy and reduce reliance on manually labeled datasets.
This development offers a potential pathway to more reliable and accessible AI-driven medical diagnostics by overcoming common limitations of existing denoising methods.
Ultrasound image analysis can become more precise and less susceptible to varying noise conditions, potentially accelerating AI adoption in clinical settings without extensive pretraining.
- · Medical AI developers
- · Healthcare providers
- · Diagnostic imaging sector
- · Traditional image denoising software providers
Improved accuracy and efficiency in ultrasound-based medical diagnoses.
Reduced need for expert human intervention in image interpretation, freeing up specialist time.
Accelerated development and deployment of AI imaging solutions across broader medical fields, potentially leading to new diagnostic tools.
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Read at arXiv cs.AI