S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging.

Area of Science:

• Medical Image Analysis
• Computer Vision
• Machine Learning

Background:

• Medical image segmentation faces challenges with limited and non-diverse training data, impacting robustness and generalizability.
• Conventional methods rely on domain knowledge, which can be unreliable or unavailable, leading to performance degradation.
• Variability in real-world inference conditions contrasts with training data limitations.

Purpose of the Study:

• To introduce a novel, domain-agnostic strategy to enhance medical image segmentation.
• To improve the robustness and generalizability of segmentation models.
• To provide a data-driven solution that complements existing segmentation training methods.

Main Methods:

• Developed 'semantic stacking,' a domain-agnostic, add-on strategy inspired by image denoising techniques.
• The method estimates a denoised semantic representation to complement conventional segmentation loss during training.
• This approach does not rely on domain-specific assumptions, ensuring broad applicability.

Main Results:

• Semantic stacking demonstrated superiority in improving segmentation performance across diverse conditions.
• The method enhanced robustness and generalizability of segmentation models.
• Extensive experiments validated the effectiveness of the proposed technique.

Conclusions:

• Semantic stacking offers a broadly applicable and effective solution for improving medical image segmentation.
• The technique addresses data scarcity and variability challenges without domain-specific constraints.
• This data-driven approach represents a significant advancement in medical image analysis.