141 citations · 332 across the 67 of their papers we have counts for
10 papers · 1 filter
A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images
Hongwei Bran Li, Chinmay Prabhakar, Suprosanna Shit +7
Quantifying the perceptual similarity of two images is a long-standing problem in low-level computer vision. The natural image domain commonly relies on supervised learning, e.g.,…
Topologically faithful image segmentation via induced matching of persistence barcodes
Nico Stucki, Johannes C. Paetzold, Suprosanna Shit +2
Image segmentation is a largely researched field where neural networks find vast applications in many facets of technology. Some of the most popular approaches to train segmentatio…
Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias
Linus Kreitner, Ivan Ezhov, Daniel Rueckert +2
Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a n…
Unsupervised Anomaly Localization with Structural Feature-Autoencoders
Felix Meissen, Johannes Paetzold, Georgios Kaissis +1
Unsupervised Anomaly Detection has become a popular method to detect pathologies in medical images as it does not require supervision or labels for training. Most commonly, the ano…
Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs
Martin J. Menten, Johannes C. Paetzold, Alina Dima +3
Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to…
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings
Florian Kofler, Ivan Ezhov, Lucas Fidon +14
Human ratings are abstract representations of segmentation quality. To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models. We eva…
A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling
Ivan Ezhov, Marcel Rosier, Lucas Zimmer +12
Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical them…
Relationformer: A Unified Framework for Image-to-Graph Generation
Suprosanna Shit, Rajat Koner, Bastian Wittmann +8
A comprehensive representation of an image requires understanding objects and their mutual relationship, especially in image-to-graph generation, e.g., road network extraction, blo…
SoK: Differential Privacy on Graph-Structured Data
Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold +2
In this work, we study the applications of differential privacy (DP) in the context of graph-structured data. We discuss the formulations of DP applicable to the publication of gra…
Differentially Private Graph Classification with GNNs
Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar +3
Graph Neural Networks (GNNs) have established themselves as the state-of-the-art models for many machine learning applications such as the analysis of social networks, protein inte…