One Model to Magnify Them All: Efficient Scale-Invariant Histopathology via Conditional Normalization and Continuous Magnification Training

2026-08-10

AI Summary

This work addresses the challenge that whole-slide images (WSIs) in digital pathology are acquired across continuously varying magnifications, while existing deep learning models are scale-sensitive and struggle to generalize to unseen or misaligned magnification levels. To overcome this limitation, the authors propose Conditional Layer Normalization (CLN), a lightweight mechanism that employs a small MLP to dynamically generate normalization parameters based on the input pixel size. Integrated into standard CNN architectures and trained on image patches sampled across a continuous range of scales, CLN enables a single model to achieve strong generalization across arbitrary magnifications. Notably, this approach is the first to cover a continuous spectrum of magnifications without requiring ensemble models. On the PANDA prostate cancer dataset, it matches or exceeds the performance of dedicated single-magnification models, consistently ranking among the top three across all evaluated magnifications—including unseen ones—while reducing both training and inference costs by 4–5×.

Abstract

Whole slide images (WSIs) in digital histopathology are acquired at discrete magnification levels encoding complementary diagnostic information from global tissue architecture to fine-grained cellular morphology. Yet, deep learning models remain sensitive to scale variation. Existing magnification-invariant methods rely on multi-scale architectures at predefined discrete resolutions, while in clinical deployment the acquisition magnification varies continuously, rarely aligns with a model's fixed training resolution, and intermediate scales are common, so robust coverage otherwise demands a costly ensemble of magnification-specific models. We propose Conditional Layer Normalization (CLN), a lightweight mechanism that generates affine normalization parameters from input pixel size via a small MLP, integrated into standard CNN architectures for both WSI classification and segmentation. Trained on patches sampled continuously across a range of pixel sizes, the model decouples inference from scanner-dependent magnification and generalizes to arbitrary, previously unseen scales at test time. On the PANDA prostate cancer dataset, our approach on average matches or exceeds independently trained single-magnification models and ranks among the top three performers at every evaluated magnification, including those unseen during training. This collapses a five-model ensemble into a single network and reduces training, and inference cost roughly 4-5 times, while leaving the multiply-accumulate count unchanged. The code is available at: https://github.com/aflorkowska/OneModelToMagnifyThemAll.

Problem

• scale-invariance
• histopathology
• magnification
• whole slide images
• deep learning

Innovation

• Conditional Layer Normalization
• scale-invariant
• continuous magnification
• histopathology
• efficient deep learning