Anees Kazi | DeepAI

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∙ 05/02/2023

Multi-Head Graph Convolutional Network for Structural Connectome Classification

We tackle classification based on brain connectivity derived from diffus...

0 Anees Kazi, et al. ∙

∙ 11/26/2022

Latent Graph Inference using Product Manifolds

Graph Neural Networks usually rely on the assumption that the graph topo...

0 Haitz Saez de Ocariz Borde, et al. ∙

∙ 07/21/2022

Unsupervised pre-training of graph transformers on patient population graphs

Pre-training has shown success in different areas of machine learning, s...

24 Chantal Pellegrini, et al. ∙

∙ 04/01/2022

Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications

Graphs are a powerful tool for representing and analyzing unstructured, ...

13 Kamilia Mullakaeva, et al. ∙

∙ 03/23/2022

Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions

Pre-training has shown success in different areas of machine learning, s...

12 Chantal Pellegrini, et al. ∙

∙ 04/08/2021

GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference

The increased amount of multi-modal medical data has opened the opportun...

0 Mahsa Ghorbani, et al. ∙

∙ 03/29/2021

IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction

Interpretability in Graph Convolutional Networks (GCNs) has been explore...

0 Anees Kazi, et al. ∙

∙ 02/27/2021

RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data

Disease prediction is a well-known classification problem in medical app...

0 Mahsa Ghorbani, et al. ∙

∙ 05/14/2020

Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)

Large-scale population-based studies in medicine are a key resource towa...

11 Gerome Vivar, et al. ∙

∙ 03/27/2020

Latent Patient Network Learning for Automatic Diagnosis

Recently, Graph Convolutional Networks (GCNs) has proven to be a powerfu...

0 Luca Cosmo, et al. ∙

∙ 02/11/2020

Differentiable Graph Module (DGM) Graph Convolutional Networks

Graph deep learning has recently emerged as a powerful ML concept allowi...

36 Anees Kazi, et al. ∙

∙ 05/08/2019

Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets

Clinical diagnostic decision making and population-based studies often r...

0 Gerome Vivar, et al. ∙

∙ 05/08/2019

Adaptive image-feature learning for disease classification using inductive graph networks

Recently, Geometric Deep Learning (GDL) has been introduced as a novel a...

0 Hendrik Burwinkel, et al. ∙

∙ 03/11/2019

InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

Geometric deep learning provides a principled and versatile manner for t...

0 Anees Kazi, et al. ∙

∙ 02/04/2019

Towards an Interactive and Interpretable CAD System to Support Proximal Femur Fracture Classification

Fractures of the proximal femur represent a critical entity in the weste...

20 Amelia Jiménez-Sánchez, et al. ∙

∙ 12/24/2018

Self-Attention Equipped Graph Convolutions for Disease Prediction

Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imagi...

0 Anees Kazi, et al. ∙

∙ 09/27/2018

Weakly-Supervised Localization and Classification of Proximal Femur Fractures

In this paper, we target the problem of fracture classification from cli...

0 Amelia Jiménez-Sánchez, et al. ∙

∙ 04/28/2018

Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction

Structural data from Electronic Health Records as complementary informat...

0 Anees Kazi, et al. ∙

∙ 12/19/2016

Cross-Modal Manifold Learning for Cross-modal Retrieval

This paper presents a new scalable algorithm for cross-modal similarity ...

0 Sailesh Conjeti, et al. ∙

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