{
  "id": 19031,
  "url": "https://arxiv.org/abs/2608.12196v1",
  "title": "M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation",
  "summary": "Purpose: Deep learning-based medical image segmentation has achieved remarkable success, yet purely data-driven approaches often fail to exploit the rich mathematical structure inherent in medical images. We investigate whether explicit mathematical inductive biases, specifically matrix spectral analysis and vector calculus operators, can enhance segmentation beyond data-driven learning alone. Methods: We propose M-Net (Math-Augmented Network), which integrates three complementary mathematical p",
  "authors": "Jing Zhu, Ye Wang, Fumin Wang",
  "category": "research",
  "topics": "healthcare,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T15:51:07.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19031",
  "original_url": "https://arxiv.org/abs/2608.12196v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}