S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging
Journal:
arXiv
Published Date:
Dec 17, 2024
Abstract
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.
