3 citations · 3 across the 7 of their papers we have counts for
9 papers
MedDiME: Efficient Latent Diffusion with Adaptive Masking for Medical Counterfactual Generation
Yan Zeng, Changlu Guo, Anders Nymark Christensen +2
Medical counterfactual generation modifies images to change model predictions for interpretability. However, existing diffusion-based approaches are often prohibitively slow and me…
FiRe: Fixed-Noise Refinement for Visual Counterfactual Explanations
Yan Zeng, Changlu Guo, Oskar Kristoffersen +3
Visual counterfactual explanations aim to change classifier decisions through realistic and localized edits while preserving decision-irrelevant content. Existing DDPM-based method…
MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanations
Changlu Guo, Anders Nymark Christensen, Anders Bjorholm Dahl +1
Visual counterfactual explanations aim to reveal the minimal semantic modifications that can alter a model's prediction, providing causal and interpretable insights into deep neura…
Learning to Build Shapes by Extrusion
Thor Vestergaard Christiansen, Karran Pandey, Alba Reinders +3
We introduce Text Encoded Extrusions (TEE), a text-based representation that expresses mesh construction as sequences of face extrusions rather than polygon lists, and a method for…
MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding
Oskar Kristoffersen, Alba Reinders Sánchez, Morten Rieger Hannemose +2
Geo-spatial analysis of our world benefits from a multimodal approach, as every single geographic location can be described in numerous ways (images from various viewpoints, textua…
SA-UNetv2: Rethinking Spatial Attention U-Net for Retinal Vessel Segmentation
Changlu Guo, Anders Nymark Christensen, Anders Bjorholm Dahl +2
Retinal vessel segmentation is essential for early diagnosis of diseases such as diabetic retinopathy, hypertension, and neurodegenerative disorders. Although SA-UNet introduces sp…
Med-Art: Diffusion Transformer for 2D Medical Text-to-Image Generation
Changlu Guo, Anders Nymark Christensen, Morten Rieger Hannemose
Text-to-image generative models have achieved remarkable breakthroughs in recent years. However, their application in medical image generation still faces significant challenges, i…
Was that so hard? Estimating human classification difficulty
Morten Rieger Hannemose, Josefine Vilsbøll Sundgaard, Niels Kvorning Ternov +2
When doctors are trained to diagnose a specific disease, they learn faster when presented with cases in order of increasing difficulty. This creates the need for automatically esti…
Multi-modal data generation with a deep metric variational autoencoder
Josefine Vilsbøll Sundgaard, Morten Rieger Hannemose, Søren Laugesen +6
We present a deep metric variational autoencoder for multi-modal data generation. The variational autoencoder employs triplet loss in the latent space, which allows for conditional…