2 citations · 2 across the 10 of their papers we have counts for
10 papers
Joint Flow Matching Enables Continuous Dose-Conditioned Cell Morphing
Lea Bogensperger, Manuela Merlo, Martin Baumgartner +2
Generative modeling has shown increasing promise for predicting cellular perturbation effects under chemical compound treatments. Existing approaches either model perturbation as a…
Sparse Concept Channels in Frozen 3D CT Vision Encoders
Farhad Nooralahzadeh, Lea Bogensperger, Christian Bluethgen +1
Large vision-language models are becoming increasingly dominant in 3D medical image interpretation, but we rarely know <i>which</i> internal units encode clinical findings or <i>wh…
Generating Physically Consistent Molecules with Energy-Based Models
Christoph Griesbacher, Lea Bogensperger, Andreas Habring +1
Molecules in equilibrium follow a Boltzmann distribution, making the underlying energy landscape a physically grounded modeling objective. However, such landscapes are difficult to…
Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization
Amaru Caceres Arroyo, Lea Bogensperger, Ahmed Allam +3
Protein fitness optimization is challenged by a vast combinatorial landscape where high-fitness variants are extremely sparse. Many current methods either underperform or require c…
Understanding, Accelerating, and Improving MeanFlow Training
Jin-Young Kim, Hyojun Go, Lea Bogensperger +5
MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the underlying training dynamics remain unclear…
A Variational Perspective on Generative Protein Fitness Optimization
Lea Bogensperger, Dominik Narnhofer, Ahmed Allam +2
The goal of protein fitness optimization is to discover new protein variants with enhanced fitness for a given use. The vast search space and the sparsely populated fitness landsca…
An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation
Lea Bogensperger, Matthias J. Ehrhardt, Thomas Pock +2
We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minim…
FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms
Lea Bogensperger, Dominik Narnhofer, Alexander Falk +2
Medical image segmentation plays an important role in accurately identifying and isolating regions of interest within medical images. Generative approaches are particularly effecti…
Learned Discretization Schemes for the Second-Order Total Generalized Variation
Lea Bogensperger, Antonin Chambolle, Alexander Effland +1
The total generalized variation extends the total variation by incorporating higher-order smoothness. Thus, it can also suffer from similar discretization issues related to isotrop…
Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions
Lea Bogensperger, Dominik Narnhofer, Filip Ilic +1
Medical image segmentation is a crucial task that relies on the ability to accurately identify and isolate regions of interest in medical images. Thereby, generative approaches all…