Selected work

Representative Papers

Calculating the Expected Value of Sample Information for Observational Studies affected by Confounding

Oct 05, 2026

Existing Expected Value of Sample Information (EVSI) methods rely on idealized randomized controlled trial assumptions, rendering them incapable of evaluating observational data affected by confounding bias. This study proposes a controllable confounded data generation framework based on Inverse Target Trial Emulation (ITTE), integrating inverse probability weighting and regression approaches to compute the EVSI of observational data. Furthermore, an efficient algorithm is developed to determine the sample size required to recover the EVSI achievable under an ideal randomized design. Empirical validation confirms that the EVSI of adjusted observational data remains lower than that of randomized data and decreases monotonically as confounding severity increases. By extending value-of-information analysis to more realistic scenarios, this work provides a novel methodological framework for assessing the value of observational studies.

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MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI

Oct 01, 2026

This study addresses evaluation bias in unsupervised anomaly detection for brain MRI caused by opaque registration, thresholding, and metric selection. We propose MIRTO, a standardized evaluation protocol incorporating label-free diagnostics, multiverse analysis, and paired bootstrap confidence intervals to quantify the impact of evaluation configurations on results, alongside a novel threshold-shift identity. Validation on BraTS 2020 reveals that axis-order mismatches can drastically reduce diffusion model AUROC, methodological differences account for most variance, and lesion sensitivity is primarily governed by its definition. Furthermore, REFLECT optimization improves Dice scores by 0.052. This work establishes a transparent and reproducible evaluation paradigm for anomaly detection.

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SWITi: Quantifying and Reducing Tiling Artifacts with Sliding Window Inner Tiling

Jul 21, 2026

This work addresses the issue of stitching artifacts—often mistaken for genuine structures—in large-image tile-based prediction, which arises from limited receptive fields and independent posterior sampling. The proposed method, SWITi, mitigates these artifacts during inference by averaging predictions over overlapping regions via a sliding window, thereby distributing discrepancies between adjacent tiles across varying locations and preventing artifact accumulation at fixed boundaries, all without requiring additional forward passes. The study introduces, for the first time, no-reference artifact evaluation metrics—FRT and ASV—and integrates pixel-gradient permutation testing to enable automatic detection and quantification of artifacts. Evaluated on both 2D and 3D fluorescence microscopy images, SWITi substantially suppresses stitching seams, enhances reconstruction fidelity and resolution, and effectively supports downstream biomedical analysis.

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Applications of temporal graph learning for predicting the dynamics of biological systems

May 27, 2026

This work addresses the limitation of existing biological foundation models, which predominantly rely on static gene expression data and thus fail to capture the dynamic evolution of gene regulatory networks (GRNs) during cellular development. The authors propose a novel approach that infers pseudotemporal trajectories from single-cell transcriptomic data, discretizes them into developmental snapshots, and reconstructs GRNs at each snapshot. A temporal graph neural network is then introduced to explicitly model the dynamic rewiring of regulatory interactions over time, enabling accurate prediction of gene expression, regulatory links, and key hub genes. To the best of our knowledge, this is the first application of temporal graph learning to single-cell biology. Evaluated on mouse erythroid gastrulation and pancreatic endocrine development datasets, the method significantly outperforms state-of-the-art foundation models such as scGPT and scFoundation across all three tasks, revealing non-trivial dynamic regulatory mechanisms.

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MamaDino: A Hybrid Vision Model for Breast Cancer 3-Year Risk Prediction

Feb 14, 2026

This work proposes MamaDino, a novel approach for breast cancer risk prediction that addresses the limitations of existing models, which rely on high-resolution mammograms and fail to explicitly model bilateral breast asymmetry—leading to performance degradation at lower resolutions. MamaDino uniquely integrates a self-supervised DINOv2 Vision Transformer with a trainable CNN encoder and introduces a BilateralMixer module to explicitly capture asymmetry between left and right breasts. Operating effectively at a reduced resolution of 512×512 (approximately 13× fewer pixels than standard inputs), the method leverages the complementary inductive biases of convolutional and transformer architectures. Evaluated on both internal and external test sets, MamaDino achieves AUCs up to 0.736, matching the performance of the current state-of-the-art model Mirai, while demonstrating robustness across diverse populations and imaging devices.

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Recent publications

Latest Papers

Calculating the Expected Value of Sample Information for Observational Studies affected by Confounding

Oct 05, 2026

Existing Expected Value of Sample Information (EVSI) methods rely on idealized randomized controlled trial assumptions, rendering them incapable of evaluating observational data affected by confounding bias. This study proposes a controllable confounded data generation framework based on Inverse Target Trial Emulation (ITTE), integrating inverse probability weighting and regression approaches to compute the EVSI of observational data. Furthermore, an efficient algorithm is developed to determine the sample size required to recover the EVSI achievable under an ideal randomized design. Empirical validation confirms that the EVSI of adjusted observational data remains lower than that of randomized data and decreases monotonically as confounding severity increases. By extending value-of-information analysis to more realistic scenarios, this work provides a novel methodological framework for assessing the value of observational studies.

0 citationsRead paper

MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI

Oct 01, 2026

This study addresses evaluation bias in unsupervised anomaly detection for brain MRI caused by opaque registration, thresholding, and metric selection. We propose MIRTO, a standardized evaluation protocol incorporating label-free diagnostics, multiverse analysis, and paired bootstrap confidence intervals to quantify the impact of evaluation configurations on results, alongside a novel threshold-shift identity. Validation on BraTS 2020 reveals that axis-order mismatches can drastically reduce diffusion model AUROC, methodological differences account for most variance, and lesion sensitivity is primarily governed by its definition. Furthermore, REFLECT optimization improves Dice scores by 0.052. This work establishes a transparent and reproducible evaluation paradigm for anomaly detection.

0 citationsRead paper

SWITi: Quantifying and Reducing Tiling Artifacts with Sliding Window Inner Tiling

Jul 21, 2026

This work addresses the issue of stitching artifacts—often mistaken for genuine structures—in large-image tile-based prediction, which arises from limited receptive fields and independent posterior sampling. The proposed method, SWITi, mitigates these artifacts during inference by averaging predictions over overlapping regions via a sliding window, thereby distributing discrepancies between adjacent tiles across varying locations and preventing artifact accumulation at fixed boundaries, all without requiring additional forward passes. The study introduces, for the first time, no-reference artifact evaluation metrics—FRT and ASV—and integrates pixel-gradient permutation testing to enable automatic detection and quantification of artifacts. Evaluated on both 2D and 3D fluorescence microscopy images, SWITi substantially suppresses stitching seams, enhances reconstruction fidelity and resolution, and effectively supports downstream biomedical analysis.

0 citationsRead paper

Applications of temporal graph learning for predicting the dynamics of biological systems

May 27, 2026

This work addresses the limitation of existing biological foundation models, which predominantly rely on static gene expression data and thus fail to capture the dynamic evolution of gene regulatory networks (GRNs) during cellular development. The authors propose a novel approach that infers pseudotemporal trajectories from single-cell transcriptomic data, discretizes them into developmental snapshots, and reconstructs GRNs at each snapshot. A temporal graph neural network is then introduced to explicitly model the dynamic rewiring of regulatory interactions over time, enabling accurate prediction of gene expression, regulatory links, and key hub genes. To the best of our knowledge, this is the first application of temporal graph learning to single-cell biology. Evaluated on mouse erythroid gastrulation and pancreatic endocrine development datasets, the method significantly outperforms state-of-the-art foundation models such as scGPT and scFoundation across all three tasks, revealing non-trivial dynamic regulatory mechanisms.

0 citationsRead paper

MamaDino: A Hybrid Vision Model for Breast Cancer 3-Year Risk Prediction

Feb 14, 2026

This work proposes MamaDino, a novel approach for breast cancer risk prediction that addresses the limitations of existing models, which rely on high-resolution mammograms and fail to explicitly model bilateral breast asymmetry—leading to performance degradation at lower resolutions. MamaDino uniquely integrates a self-supervised DINOv2 Vision Transformer with a trainable CNN encoder and introduces a BilateralMixer module to explicitly capture asymmetry between left and right breasts. Operating effectively at a reduced resolution of 512×512 (approximately 13× fewer pixels than standard inputs), the method leverages the complementary inductive biases of convolutional and transformer architectures. Evaluated on both internal and external test sets, MamaDino achieves AUCs up to 0.736, matching the performance of the current state-of-the-art model Mirai, while demonstrating robustness across diverse populations and imaging devices.

0 citationsRead paper