446 citations · 2.3k across the 135 of their papers we have counts for
11 papers · 1 filter
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl +2
Access to sufficient annotated data is a common challenge in training deep neural networks on medical images. As annotating data is expensive and time-consuming, it is difficult fo…
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…
Learning Interpretable Disentangled Representations using Adversarial VAEs
Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab +1
Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning…
Semi-Supervised Few-Shot Learning with Prototypical Random Walks
Ahmed Ayyad, Yuchen Li, Nassir Navab +2
Recent progress has shown that few-shot learning can be improved with access to unlabelled data, known as semi-supervised few-shot learning(SS-FSL). We introduce an SS-FSL approach…
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness
Magdalini Paschali, Walter Simson, Abhijit Guha Roy +4
In this paper we propose a novel augmentation technique that improves not only the performance of deep neural networks on clean test data, but also significantly increases their ro…
Self-Attention Equipped Graph Convolutions for Disease Prediction
Anees Kazi, S. Arvind krishna, Shayan Shekarforoush +3
Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imaging (clinical test, demographics, etc.) data can be collected together and used for disease prediction. Such…
Adversarial Signal Denoising with Encoder-Decoder Networks
Leslie Casas, Attila Klimmek, Nassir Navab +1
The presence of noise is common in signal processing regardless the signal type. Deep neural networks have shown good performance in noise removal, especially on the image domain.…
Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction
Anees Kazi, Shadi Albarqouni, Karsten Kortuem +1
Structural data from Electronic Health Records as complementary information to imaging data for disease prediction. We incorporate novel weighting layer into the Graph Convolutiona…
Learning-based Surgical Workflow Detection from Intra-Operative Signals
Ralf Stauder, Ergün Kayis, Nassir Navab
A modern operating room (OR) provides a plethora of advanced medical devices. In order to better facilitate the information offered by them, they need to automatically react to the…
An Efficient Training Algorithm for Kernel Survival Support Vector Machines
Sebastian Pölsterl, Nassir Navab, Amin Katouzian
Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support. In order to leverage large a…