mlsanityData to 14 Oct 2026

S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging

arXiv 2412.13156 ↗First posted 17 Dec 2024cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 43 of its 61 references that are in the map. First posted on arXiv 17 Dec 2024.

Builds on

43 of 61 references.

References · 43

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31 more
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Citing papers per year: 2025: 2.

2 papers.

  1. 2025IntelliCardiac: An Intelligent Platform for Cardiac Image Segmentation and Classificationcited by 0
  2. 2025Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRIcited by 0