6 citations · 13 across the 23 of their papers we have counts for
30 papers
SciFusion: Physics-Guided Compositional Diffusion for Multimodal 3D Scientific Image Generation
Luoxi Wang, Alessio Quercia, Hanno Scharr +1
Existing 3D generative approaches are typically developed for individual imaging modalities, requiring substantial modality-specific data and limiting their ability to exploit shar…
AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture
Shengjie Guo, Xin Li, Borjana Arsova +2
Accurate counting and localization of plants and their organs support phenotyping and yield estimation, yet target appearance, scale, and density vary widely across species and ima…
Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion
Christian Heinemann, Freddie Åström, George Baravdish +3
In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel…
RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data
Xuan Zhao, Lena Krieger, Zhuo Cao +3
Counterfactual explanations (CFs) help understand machine learning models by identifying minimal input changes that would lead to alternative model outcomes. Recent work demonstrat…
Residual-Space Evolutionary Optimization via Flow-based Generative Models
Zhuo Cao, Lena Krieger, Fernanda Nader +3
Data editing with generative methods typically requires differentiable objectives and gradient-based search. However, these assumptions break down in flow-based settings, where edi…
DART: A design-aware microfluidic chip paradigm for real-time live-cell image analysis
Johannes Seiffarth, Matthias Pesch, Lukas Scholtes +3
High-throughput microfluidic live-cell imaging generates rich single-cell data. Yet semi-automated procedures for locating regions of interest (RoIs), each containing one cell popu…
Counterfactual Transport Flows for Offline Conservative Trajectory Refinement
Lena Krieger, Xuan Zhao, Zhuo Cao +3
Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback. A key diffi…
Higher-Order Hit-&-Run Samplers for Linearly Constrained Densities
Richard D. Paul, Anton Stratmann, Johann F. Jadebeck +4
Markov chain Monte Carlo (MCMC) sampling of densities restricted to linearly constrained domains is an important task arising in Bayesian treatment of inverse problems in the natur…
Least but not Last: Fine-tuning Intermediate Principal Components for Better Performance-Forgetting Trade-Offs
Alessio Quercia, Arya Bangun, Ira Assent +1
Low-Rank Adaptation (LoRA) methods have emerged as crucial techniques for adapting large pre-trained models to downstream tasks under computational and memory constraints. However,…
Classifier Reconstruction Through Counterfactual-Aware Wasserstein Prototypes
Xuan Zhao, Zhuo Cao, Arya Bangun +2
Counterfactual explanations provide actionable insights by identifying minimal input changes required to achieve a desired model prediction. Beyond their interpretability benefits,…
Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imaging
Laurentius Valdy, Richard D. Paul, Alessio Quercia +4
Accurate multi-slice reconstruction from limited measurement data is crucial to speed up the acquisition process in medical and scientific imaging. However, it remains challenging…
FlowTIE: Flow-based Transport of Intensity Equation for Phase Gradient Estimation from 4D-STEM Data
Arya Bangun, Maximilian Töllner, Xuan Zhao +2
We introduce FlowTIE, a neural-network-based framework for phase reconstruction from 4D-Scanning Transmission Electron Microscopy (STEM) data, which integrates the Transport of Int…
LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching
Zhuo Cao, Xuan Zhao, Lena Krieger +2
The growing integration of machine learning (ML) and artificial intelligence (AI) models into high-stakes domains such as healthcare and scientific research calls for models that a…
Galaxy Morphology Classification with Counterfactual Explanation
Zhuo Cao, Lena Krieger, Hanno Scharr +1
Galaxy morphologies play an essential role in the study of the evolution of galaxies. The determination of morphologies is laborious for a large amount of data giving rise to machi…
EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis
Nils Friederich, Angelo Jovin Yamachui Sitcheu, Annika Nassal +12
Microfluidic Live-Cell Imaging (MLCI) yields data on microbial cell factories. However, continuous acquisition is challenging as high-throughput experiments often lack real-time in…
Efficient Epistemic Uncertainty Estimation in Cerebrovascular Segmentation
Omini Rathore, Richard Paul, Abigail Morrison +2
Brain vessel segmentation of MR scans is a critical step in the diagnosis of cerebrovascular diseases. Due to the fine vessel structure, manual vessel segmentation is time consumin…
How To Make Your Cell Tracker Say "I dunno!"
Richard D. Paul, Johannes Seiffarth, David Rügamer +2
Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analy…
1LoRA: Summation Compression for Very Low-Rank Adaptation
Alessio Quercia, Zhuo Cao, Arya Bangun +4
Parameter-Efficient Fine-Tuning (PEFT) methods have transformed the approach to fine-tuning large models for downstream tasks by enabling the adjustment of significantly fewer para…
Self-Supervised Learning Based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation
Qin Wang, Alessio Quercia, Benjamin Bruns +3
Self-supervised learning (SSL) methods have achieved remarkable success in learning image representations allowing invariances in them - but therefore discarding transformation inf…
Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks
Alessio Quercia, Erenus Yildiz, Zhuo Cao +4
Monocular depth estimation (MDE) is a challenging task in computer vision, often hindered by the cost and scarcity of high-quality labeled datasets. We tackle this challenge using…
MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)
Arya Bangun, Zhuo Cao, Alessio Quercia +2
Magnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However, t…
Equivariant Representation Learning for Augmentation-based Self-Supervised Learning via Image Reconstruction
Qin Wang, Kai Krajsek, Hanno Scharr
Augmentation-based self-supervised learning methods have shown remarkable success in self-supervised visual representation learning, excelling in learning invariant features but of…
Retrieval of sun-induced plant fluorescence in the O-A absorption band from DESIS imagery
Jim Buffat, Miguel Pato, Kevin Alonso +7
We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation () to high-quality airborne estimates of sun-induc…
EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis
Nils Friederich, Angelo Jovin Yamachui Sitcheu, Annika Nassal +12
Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data…