127 citations · 412 across the 19 of their papers we have counts for
20 papers
On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions
Razieh Rezaei, Alireza Dizaji, Ashkan Khakzar +3
Neural networks are increasingly finding their way into the realm of graphs and modeling relationships between features. Concurrently graph neural network explanation approaches ar…
Multi-Head Graph Convolutional Network for Structural Connectome Classification
Anees Kazi, Jocelyn Mora, Bruce Fischl +2
We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks…
Latent Graph Inference using Product Manifolds
Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero +1
Graph Neural Networks usually rely on the assumption that the graph topology is available to the network as well as optimal for the downstream task. Latent graph inference allows m…
Unsupervised pre-training of graph transformers on patient population graphs
Chantal Pellegrini, Nassir Navab, Anees Kazi
Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been ful…
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…
Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions
Chantal Pellegrini, Anees Kazi, Nassir Navab
Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging. However, it has not been…
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
Mahsa Ghorbani, Mojtaba Bahrami, Anees Kazi +3
The increased amount of multi-modal medical data has opened the opportunities to simultaneously process various modalities such as imaging and non-imaging data to gain a comprehens…
IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction
Anees Kazi, Soroush Farghadani, Nassir Navab
Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in computer vision in general, yet, in the medical domain, it requires further examination.…
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
Mahsa Ghorbani, Anees Kazi, Mahdieh Soleymani Baghshah +2
Disease prediction is a well-known classification problem in medical applications. GCNs provide a powerful tool for analyzing the patients' features relative to each other. This ca…
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…
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…
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni +5
We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray ima…
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…
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni +5
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system. Although primarily dealing wi…
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…
Cross-Modal Manifold Learning for Cross-modal Retrieval
Sailesh Conjeti, Anees Kazi, Nassir Navab +1
This paper presents a new scalable algorithm for cross-modal similarity preserving retrieval in a learnt manifold space. Unlike existing approaches that compromise between preservi…