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- Luc Van GoolSuggested from co-authorship
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Works31 from public data
- 360
- Building Deep Networks on Grassmann Manifolds206
This paper proposes a deep network architecture by generalizing the Euclidean network paradigm to Grassmann manifolds and designs full rank mapping layers to transform input Grassmannian data to more desirable ones, and exploits re-orthonormalization layers to normalize the resulting matrices.
- 168
- Wasserstein Divergence for GANs167
A novel Wasserstein divergence (W-div) is proposed, which is a relaxed version of W-met and does not require the k-Lipschitz constraint and is introduced as a concrete application, which can faithfully approximate W-div through optimization.
- Sliced Wasserstein Generative Models150
This paper proposes to approximate SWDs with a small number of parameterized orthogonal projections in an end-to-end deep learning fashion and designs two types of differentiable SWD blocks to equip modern generative frameworks---Auto-Encoders and Generative Adversarial Networks.
- 53
- Towards generative digital twins in biomedical research38
Generative digital twins in biomedical research enable compelling in silico modeling for simulated interventions, facilitating the exploration of ‘what if’ causal scenarios for clinical diagnostics and treatments tailored to individual patients.
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- Automated causal inference in application to randomized controlled clinical trials21
It is shown that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data via suppressing the causal probability of non-causal variables by a wide margin.
- Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets21
This paper formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) colorimage generation, and diffusion-tensor (DT) image generation and introduces a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds.
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- Generic 3D Convolutional Fusion for Image Restoration6
The proposed 3DCF method uses the exact same convolutional network architecture to address both image denoising and single image super-resolution, and achieves substantial improvements over the state of the art methods that it fuses on standard benchmarks for both tasks.
- SST-editing: in silico spatial transcriptomic editing at single-cell resolution5
This work trains the approach under the framework of GAN (Inversion), and successfully model the transition from tumor to normal tissue samples, as measured with quantifiable and interpretable cellular features.
- Towards IID representation learning and its application on biomedical data5
By utilizing the representation obtained via the learned function that induces IID, prediction of molecular characteristics on two biomedical datasets with real-world distribution shifts introduced by a) preanalytical variation and b) sampling protocol is conducted.
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- Manifold-valued Image Generation with Wasserstein Adversarial Networks.4
This paper makes the first attempt to formulate the problem of generating manifold-valued images, which are frequently encountered in real-world applications and generalize the state-of-the-art technique of Wasserstein GANs to the manifold context with exploiting Riemannian geometry.
- IST-editing: Infinite spatial transcriptomic editing in a generated gigapixel mouse pup3
This work introduces Infinite Spatial Transcriptomic editing (IST-editing) and establishes gene expression-guided editing in a generated gigapixel mouse pup and discusses and evaluates editing effects on interpretable morphological features.
- GILEA: GAN Inversion-enabled latent eigenvalue analysis for phenome profiling and editing3
A novel GAN Inversion-enabled Latent Eigenvalue Analysis (GILEA) framework is proposed and applied to phenome profiling and editing and demonstrates the power of GILEA using publicly available SARS-CoV-2 datasets stained with the multiplexed fluorescence cell-painting protocol.
- Tera-MIND: Tera-scale mouse brain simulation via spatial mRNA-guided diffusion2
Tera-MIND is proposed, a novel generative framework capable of simulating Tera-scale mouse brains in 3D using a patch-based and boundary-aware diffusion model and offers an efficient generative modeling of whole virtual organisms, paving the way for integrative applications in biomedical research.
- GILEA: In silico phenome profiling and editing using GAN Inversion2
A novel GAN Inversion-enabled Latent Eigenvalue Analysis (GILEA) framework is proposed and applied to in silico phenome profiling and editing and can be biologically supported by editing of the latent representations and simulation of dynamic phenotype transitions between physiological and pathological states.
- Generative cerebral vasculature visualization using spatial transcriptomic data–
These findings demonstrate the capability of generative AI models trained on single-cell spatial transcriptomic data to reconstruct biologically meaningful higher-order structures and highlight the potential of such models as in silico systems for high-throughput exploration of vascular function and its dysregulation in neurological disorders.
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- Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, -2 and -3 trials–
This study shows that advanced age is associated with more aggressive tumour features, and independently and causally related to worse oncological outcomes, and treatment for endometrial cancer in elderly women should not be de-escalated based on their age alone.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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