AI RESEARCH
SCKAN: Structural Consensus-based KAN Prototype Learning for Semi-Supervised Pancreas Segmentation
arXiv CS.CV
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ArXi:2605.27032v1 Announce Type: new Accurate pancreas segmentation is critical for early cancer diagnosis, where annotation scarcity necessitates Semi-Supervised Learning (SSL). However, due to significant inter-sample morphological variability, existing SSL methods face severe generalizability limitations under sparse supervision, leading to the Supervision Bias problem.