Authors
Yanxia Wang, Xinghua Ren, Xiaohui Li
Published in
Scientific reports. Jun 27, 2026. Epub Jun 27, 2026.
Abstract
Early and accurate detection of brain tumors is clinically valuable for improving prognosis and guiding treatment. Existing deep-learning methods for magnetic resonance imaging (MRI) brain tumor detection face three difficulties: weak texture at lesion boundaries impairs localization; heterogeneous lesion scales degrade multi-scale detection; and non-maximum suppression (NMS) post-processing limits end-to-end inference. We propose a topology-decoupled end-to-end detection framework based on boundary-preserving feature flow and inter-channel correlation (ICC) distillation. A high-capacity teacher combines a multi-gradient-flow backbone with a gather-and-distribute global fusion mechanism, capturing both pathological boundary textures and anatomical context; a lightweight student is then derived by removing the global-fusion neck while retaining the isomorphic backbone. After comparing five feature distillation methods, we adopt ICC distillation, which aligns Gram matrices of intermediate features and mitigates the background-dominated bias common in medical imaging. Across three random seeds, the ICC-distilled student reaches [email protected] = [Formula: see text], surpassing the plain student ([Formula: see text]) and matching or exceeding the teacher ([Formula: see text]). On the BraTS small-lesion stratum it attains 98.7% lesion recall with a false-positive-per-image rate of 0.014. The student achieves this at low cost (6.09M parameters, 11.7 GFLOPs, 168 FPS), suiting resource-constrained clinical deployment.
PMID:
42365106
Bibliographic data and abstract were imported from PubMed on 28 Jun 2026.
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