{
  "id": 14859,
  "url": "https://arxiv.org/abs/2607.26090v1",
  "title": "Shape-Based Inductive Bias for Glioma Grading from Tumor Contours",
  "summary": "Glioma grading from tumor contours is often treated as a pixel problem even when the signal of interest is shape. We align closed contours with a functional shape-alignment framework, separate global deformation from residual Fourier shape, and organize these quantities as frequency-ordered tokens. In five-fold patient-disjoint cross-validation on BraTS~2020 tumor contours, with model selection performed using grouped inner validation, a compact multilayer perceptron (MLP) achieves the highest m",
  "authors": "Puneet Velidi, Michelle F. Miranda, Farouk Nathoo, Ashery Mbilinyi, Cédric Beaulac",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T19:11:50.000Z",
  "fetched_at": "2026-07-30T05:10:24.387Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14859",
  "original_url": "https://arxiv.org/abs/2607.26090v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}