AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
AMN (Adaptive Multi-Scale Nuclei Network) is a dual-encoder segmentation framework that combines Swin Transformer and ResNet-50 feature pyramid via a learned per-channel gating mechanism. It achieves mean Dice 0.82 and mean F1 0.68 on CoNIC benchmark, outperforming eight baselines, and shows strong generalization on MoNuSeg.
[2606.07633] AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
[Submitted on 31 May 2026]
Title:AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
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Abstract:Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction. Existing approaches rely on either convolutional or transformer-based encoders in isolation, limiting their ability to simultaneously capture fine-grained local texture and long-range spatial context. We present AMN (Adaptive Multi-Scale Nuclei Network), a dual-encoder segmentation framework that jointly leverages a Swin Transformer and a ResNet-50 feature pyramid, fused via a learned per-channel gating mechanism that dynamically weighs each encoder's contribution at every scale. AMN is trained with a multi-objective loss combining class-weighted focal loss, boundary-aware loss with positive-pixel emphasis, and a novel uncertainty-modulated classification term that suppresses overconfident erroneous predictions. Evaluated on the CoNIC benchmark across seven nuclei classes, AMN achieves a mean Dice of 0.82 and mean F1 of 0.68, with an F1 of 0.67 on the diagnostically challenging lymphocyte class. AMN outperforms eight baseline models spanning pure-CNN, pure-transformer, and recent hybrid architectures: U-Net, ResU-Net, DeepLabV3+, SegNet, ViT-Small, HmsU-Net, ConvFormer-UNet, and BEFUnet. Cross-dataset evaluation on MoNuSeg demonstrates strong generalization without retraining and validating the domain robustness of the learned representations.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
MSC classes: 68T07, 92C55, 68U10
ACM classes: I.4.6; I.4.8; I.2.10
Cite as: arXiv:2606.07633 [cs.CV]
(or arXiv:2606.07633v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.07633
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Spoorthi M [view email] [v1] Sun, 31 May 2026 06:46:23 UTC (1,171 KB)
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