8 citations · 10 across the 11 of their papers we have counts for
9 papers · 1 filter
Sparse cubical complexes for efficient topology-preservation in image data
Alexander H. Berger, Marco Fontana, Daniel Rueckert +3
Persistent homology (PH) is a frequently used tool for extracting and preserving topological information from image data, particularly in image segmentation, where preservation of…
Beyond scalar losses: calibrating segmentation models via gradient vector field surgery
Laurin Lux, Alexander H. Berger, Moritz Knolle +2
Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced segmentation tasks. However, models…
Synthetic Vasculature and Pathology Enhance Vision-Language Model Reasoning
Chenjun Li, Cheng Wan, Laurin Lux +4
Vision-Language Models (VLMs) offer a promising path toward interpretable medical diagnosis by allowing users to ask about clinical explanations alongside predictions and across di…
Addressing Annotation Scarcity in Hyperspectral Brain Image Segmentation with Unsupervised Domain Adaptation
Tim Mach, Daniel Rueckert, Alex Berger +2
This work presents a novel deep learning framework for segmenting cerebral vasculature in hyperspectral brain images. We address the critical challenge of severe label scarcity, wh…
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis
Chenjun Li, Laurin Lux, Alexander H. Berger +3
Accurate staging of Diabetic Retinopathy (DR) is essential for guiding timely interventions and preventing vision loss. However, current staging models are hardly interpretable, an…
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Laurin Lux, Alexander H. Berger, Maria Romeo Tricas +8
Interpretability is crucial for utilizing machine learning models as clinical decision support tools for medical diagnostics. However, most state-of-the-art image classifiers based…
Pitfalls of topology-aware image segmentation
Alexander H. Berger, Laurin Lux, Alexander Weers +3
Topological correctness, i.e., the preservation of structural integrity and specific characteristics of shape, is a fundamental requirement for medical imaging tasks, such as neuro…
Topograph: An efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation
Laurin Lux, Alexander H. Berger, Alexander Weers +4
Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological…
Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers
Alexander H. Berger, Laurin Lux, Suprosanna Shit +5
Direct image-to-graph transformation is a challenging task that involves solving object detection and relationship prediction in a single model. Due to this task's complexity, larg…