186 citations · 224 across the 9 of their papers we have counts for
14 papers
DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation
Yousef Yeganeh, Azade Farshad, Peter Weinberger +3
Although purely transformer-based architectures showed promising performance in many computer vision tasks, many hybrid models consisting of CNN and transformer blocks are introduc…
Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Jianning Li, Jana Fragemann, Seyed-Ahmad Ahmadi +2
The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in $β…
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
Kamilia Mullakaeva, Luca Cosmo, Anees Kazi +3
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property predict…
Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time
Gerome Vivar, Kamilia Mullakaeva, Andreas Zwergal +2
Computer-aided diagnosis (CADx) algorithms in medicine provide patient-specific decision support for physicians. These algorithms are usually applied after full acquisition of high…
Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets
Gerome Vivar, Hendrik Burwinkel, Anees Kazi +3
Clinical diagnostic decision making and population-based studies often rely on multi-modal data which is noisy and incomplete. Recently, several works proposed geometric deep learn…
Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks
Hendrik Burwinkel, Anees Kazi, Gerome Vivar +4
Recently, Geometric Deep Learning (GDL) has been introduced as a novel and versatile framework for computer-aided disease classification. GDL uses patient meta-information such as…