ArticleGigaScience2026
FPNuNet: a frequency-aware prompt-guided network for nuclear segmentation and classification in immunohistochemistry images.
Article in GigaScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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6 authors.
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Abstract
backgroundAccurate nuclear segmentation and classification (NuSC) in immunohistochemistry (IHC)-stained whole-slide images is essential for reliable biomarker quantification in computational pathology. Although existing NuSC methods perform well on Hematoxylin and Eosin images, they frequently underperform on IHC-stained images owing to stain heterogeneity, low nuclear contrast, and scarce biomarker-specific annotations. Prior efforts based on stain normalization or cross-domain knowledge transfer have yielded only marginal improvements under these conditions. To address these limitations, we introduce FPNuNet, a frequency-aware prompt-guided network tailored for NuSC of IHC-stained images.
resultsFPNuNet extracts multi-source features through 4 parallel encoders: a frozen SAM-based structural encoder and a frozen UNI-based semantic encoder-both adapted via lightweight discrete cosine transform-based prompt generators-together with a wavelet feature encoder and a multi-scale context encoder that capture complementary spatial and frequency-domain descriptors from the raw input. A discrete cosine transform-enabled frequency-aware fusion neck integrates all 4 feature streams with spectral enhancement, and 3 collaborative decoder branches jointly predict binary masks, horizontal-vertical vectors, and nuclear types. FPNuNet is evaluated on CD47-IHCNuSC, a dataset comprising 86 CD47-stained esophageal cancer patches with 18,483 manually annotated nuclei spanning 7 clinically meaningful subtypes. On CD47-IHCNuSC, FPNuNet achieves the best instance-segmentation and aggregate subtype-classification performance among all evaluated methods under challenging IHC staining conditions.
conclusionsThese results demonstrate the effectiveness of frequency-aware prompt-guided feature learning for nuclear segmentation and classification in IHC-stained images and suggest that FPNuNet provides a promising framework for robust biomarker-oriented computational pathology analysis.
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