S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging
This addresses the challenge of limited and variable training data in medical imaging, offering a broadly applicable solution that is incremental as it builds on existing segmentation frameworks.
The paper tackles the problem of robustness and generalizability in medical image segmentation by introducing a domain-agnostic method called semantic stacking, which improves segmentation performance across diverse conditions without relying on domain-specific knowledge.
Robustness and generalizability in medical image segmentation are often hindered by scarcity and limited diversity of training data, which stands in contrast to the variability encountered during inference. While conventional strategies -- such as domain-specific augmentation, specialized architectures, and tailored training procedures -- can alleviate these issues, they depend on the availability and reliability of domain knowledge. When such knowledge is unavailable, misleading, or improperly applied, performance may deteriorate. In response, we introduce a novel, domain-agnostic, add-on, and data-driven strategy inspired by image stacking in image denoising. Termed ``semantic stacking,'' our method estimates a denoised semantic representation that complements the conventional segmentation loss during training. This method does not depend on domain-specific assumptions, making it broadly applicable across diverse image modalities, model architectures, and augmentation techniques. Through extensive experiments, we validate the superiority of our approach in improving segmentation performance under diverse conditions. Code is available at https://github.com/ymp5078/Semantic-Stacking.