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arXiv AI By Zhu Zhu, Shuo Jiang, Jingyuan Zheng, Yawen Li, Yifei Chen, Manli Zhao, Weizhong Gu, Feiwei Qin, Jinhu Wang, Gang Yu

Towards Accurate and Lightweight Peripheral Neuroblastic Tumor Diagnosis via Contrastive Multi-scale Pathological Image Analysis

Read the original on arXiv AI →

The paper introduces CoPath, a lightweight framework for diagnosing peripheral neuroblastic tumors (pNTs) from whole-slide images. CoPath combines CoHisNet, a multi‑scale feature‑fusion network that replaces traditional MLPs with Kolmogorov‑Arnold Network layers for efficient nonlinear modeling, and PathVote, which aggregates patch‑level predictions using pathology‑informed priors. Experiments on a private pNT cohort and the public BreakHis dataset show that CoPath matches or surpasses existing classifiers while reducing computational complexity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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