Institution profile

Sano Centre for Computational Medicine

Academic institutionnorthamerica · ca
Official website
Research library5linked papers
Opportunities0open roles
Selected work

Representative Papers

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

Aug 10, 2026

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×.

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A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data

Feb 19, 2026

Clinical data are frequently compromised by missing values, leading to machine learning models with unstable features, poor interpretability, and insufficient robustness—limitations that hinder their deployment in high-stakes clinical decision-making. To address this challenge, this work proposes CACTUS, a novel framework that uniquely prioritizes feature stability as a core evaluation criterion. By integrating feature abstraction, interpretable classification, and systematic stability analysis, CACTUS enables trustworthy predictions even with small-scale, heterogeneous, and incomplete clinical datasets. Evaluated on a cohort of 568 hematuria patients, CACTUS achieves competitive or superior predictive performance while substantially enhancing the stability of key features under missing data conditions. Notably, it demonstrates robustness in sex-stratified analyses, thereby improving the model’s clinical credibility and reproducibility.

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Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?

Oct 08, 2025

In federated learning (FL) for medical imaging, non-IID data arising from inter-site MRI intensity normalization discrepancies—coupled with privacy-preserving constraints that limit data sharing—severely hinder model generalizability and performance. Method: This work systematically evaluates the impact of diverse intensity normalization strategies on 3D brain tumor segmentation and proposes a privacy-preserving FL framework tailored to multi-center heterogeneous data. It identifies normalization choice as a primary driver of client-wise distribution shift and introduces a robust FL training strategy adaptive to heterogeneous intensity distributions. Contribution/Results: Without moving raw data from local sites, the proposed method achieves a 92% 3D Dice score on the BraTS test set—matching centralized training performance—and provides the first empirical validation that high-fidelity, privacy-compliant 3D medical image segmentation is feasible under realistic, clinically observed normalization heterogeneity.

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Recent publications

Latest Papers

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

Aug 10, 2026

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×.

0 citationsRead paper

A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data

Feb 19, 2026

Clinical data are frequently compromised by missing values, leading to machine learning models with unstable features, poor interpretability, and insufficient robustness—limitations that hinder their deployment in high-stakes clinical decision-making. To address this challenge, this work proposes CACTUS, a novel framework that uniquely prioritizes feature stability as a core evaluation criterion. By integrating feature abstraction, interpretable classification, and systematic stability analysis, CACTUS enables trustworthy predictions even with small-scale, heterogeneous, and incomplete clinical datasets. Evaluated on a cohort of 568 hematuria patients, CACTUS achieves competitive or superior predictive performance while substantially enhancing the stability of key features under missing data conditions. Notably, it demonstrates robustness in sex-stratified analyses, thereby improving the model’s clinical credibility and reproducibility.

0 citationsRead paper

Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?

Oct 08, 2025

In federated learning (FL) for medical imaging, non-IID data arising from inter-site MRI intensity normalization discrepancies—coupled with privacy-preserving constraints that limit data sharing—severely hinder model generalizability and performance. Method: This work systematically evaluates the impact of diverse intensity normalization strategies on 3D brain tumor segmentation and proposes a privacy-preserving FL framework tailored to multi-center heterogeneous data. It identifies normalization choice as a primary driver of client-wise distribution shift and introduces a robust FL training strategy adaptive to heterogeneous intensity distributions. Contribution/Results: Without moving raw data from local sites, the proposed method achieves a 92% 3D Dice score on the BraTS test set—matching centralized training performance—and provides the first empirical validation that high-fidelity, privacy-compliant 3D medical image segmentation is feasible under realistic, clinically observed normalization heterogeneity.

0 citationsRead paper