643 citations · 1.1k across the 21 of their papers we have counts for
22 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…
Open-Source Skull Reconstruction with MONAI
Jianning Li, André Ferreira, Behrus Puladi +7
We present a deep learning-based approach for skull reconstruction for MONAI, which has been pre-trained on the MUG500+ skull dataset. The implementation follows the MONAI contribu…
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…
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
Gerome Vivar, Anees Kazi, Hendrik Burwinkel +3
Large-scale population-based studies in medicine are a key resource towards better diagnosis, monitoring, and treatment of diseases. They also serve as enablers of clinical decisio…
Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions
Hendrik Burwinkel, Holger Matz, Stefan Saur +6
Today, cataract surgery is the most frequently performed ophthalmic surgery in the world. The cataract, a developing opacity of the human eye lens, constitutes the world's most fre…
Decision Support for Intoxication Prediction Using Graph Convolutional Networks
Hendrik Burwinkel, Matthias Keicher, David Bani-Harouni +4
Every day, poison control centers (PCC) are called for immediate classification and treatment recommendations if an acute intoxication is suspected. Due to the time-sensitive natur…
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…
Latent-Graph Learning for Disease Prediction
Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi +2
Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction. A key component in thes…
Differentiable Graph Module (DGM) for Graph Convolutional Networks
Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi +2
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. Such methods have sh…
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…
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Anees Kazi, Shayan shekarforoush, S. Arvind krishna +6
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks (GC…
Stabilizing Inputs to Approximated Nonlinear Functions for Inference with Homomorphic Encryption in Deep Neural Networks
Moustafa AboulAtta, Matthias Ossadnik, Seyed-Ahmad Ahmadi
Leveled Homomorphic Encryption (LHE) offers a potential solution that could allow sectors with sensitive data to utilize the cloud and securely deploy their models for remote infer…
TOMAAT: volumetric medical image analysis as a cloud service
Fausto Milletari, Johann Frei, Seyed-Ahmad Ahmadi
Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art,…
Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion
Gerome Vivar, Andreas Zwergal, Nassir Navab +1
In large population-based studies and in clinical routine, tasks like disease diagnosis and progression prediction are inherently based on a rich set of multi-modal data, including…
Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning
Atanas Mirchev, Seyed-Ahmad Ahmadi
In recent years, Generative Adversarial Networks (GAN) have emerged as a powerful method for learning the mapping from noisy latent spaces to realistic data samples in high-dimensi…
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
Patrick Ferdinand Christ, Florian Ettlinger, Felix Grün +17
Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision su…
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks
Patrick Ferdinand Christ, Florian Ettlinger, Georgios Kaissis +7
Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the potential to substantially enhance tumor treatment strategies for HCC patients. In this work…
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Patrick Ferdinand Christ, Mohamed Ezzeldin A. Elshaer, Florian Ettlinger +10
Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support…
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most a…
Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound
Fausto Milletari, Seyed-Ahmad Ahmadi, Christine Kroll +8
In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs). Our method is based on Hough vo…