1 code implementation • 1 Jan 2024 • Razieh Rezaei, Alireza Dizaji, Ashkan Khakzar, Anees Kazi, Nassir Navab, Daniel Rueckert
In this work, we assess attribution methods from a perspective not previously explored in the graph domain: retraining.
no code implementations • 2 May 2023 • Anees Kazi, Jocelyn Mora, Bruce Fischl, Adrian V. Dalca, Iman Aganj
To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification.
no code implementations • 26 Nov 2022 • Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Liò
The original dDGM architecture used the Euclidean plane to encode latent features based on which the latent graphs were generated.
2 code implementations • 21 Jul 2022 • Chantal Pellegrini, Nassir Navab, Anees Kazi
We find that our proposed pre-training methods help in modeling the data at a patient and population level and improve performance in different fine-tuning tasks on all datasets.
no code implementations • 1 Apr 2022 • Kamilia Mullakaeva, Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein
In this work, we propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications that exploits the graph representation of the input data samples and their latent relation.
2 code implementations • 23 Mar 2022 • Chantal Pellegrini, Anees Kazi, Nassir Navab
We test our method on two medical datasets of patient records, TADPOLE and MIMIC-III, including imaging and non-imaging features and different prediction tasks.
Ranked #1 on Length-of-Stay prediction on MIMIC-III
1 code implementation • 8 Apr 2021 • Mahsa Ghorbani, Mojtaba Bahrami, Anees Kazi, Mahdieh SoleymaniBaghshah, Hamid R. Rabiee, Nassir Navab
The soft pseudo-labels are then used to train a deep student network for disease prediction of unseen test data for which the graph modality is unavailable.
no code implementations • 29 Mar 2021 • Anees Kazi, Soroush Farghadani, Nassir Navab
The main novelty lies in the interpretable attention module (IAM), which directly operates on multi-modal features.
1 code implementation • 27 Feb 2021 • Mahsa Ghorbani, Anees Kazi, Mahdieh Soleymani Baghshah, Hamid R. Rabiee, Nassir Navab
This is accomplished by associating a graph-based neural network to each class, which is responsible for weighting the class samples and changing the importance of each sample for the classifier.
1 code implementation • 14 May 2020 • Gerome Vivar, Anees Kazi, Hendrik Burwinkel, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi
As a solution, we propose an end-to-end learning of imputation and disease prediction of incomplete medical datasets via Multigraph Geometric Matrix Completion (MGMC).
no code implementations • 27 Mar 2020 • Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein
Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction.
1 code implementation • 11 Feb 2020 • Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein
We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning).
no code implementations • 8 May 2019 • Hendrik Burwinkel, Anees Kazi, Gerome Vivar, Shadi Albarqouni, Guillaume Zahnd, Nassir Navab, Seyed-Ahmad Ahmadi
We propose a new network architecture that exploits an inductive end-to-end learning approach for disease classification, where filters from both the CNN and the graph are trained jointly.
no code implementations • 8 May 2019 • Gerome Vivar, Hendrik Burwinkel, Anees Kazi, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi
Recently, several works proposed geometric deep learning approaches to solve disease classification, by modeling patients as nodes in a graph, along with graph signal processing of multi-modal features.
no code implementations • 11 Mar 2019 • Anees Kazi, Shayan shekarforoush, S. Arvind krishna, Hendrik Burwinkel, Gerome Vivar, Karsten Kortuem, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain.
no code implementations • 4 Feb 2019 • Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni, Chlodwig Kirchhoff, Peter Biberthaler, Nassir Navab, Sonja Kirchhoff, Diana Mateus
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 images according to the AO classification.
no code implementations • 24 Dec 2018 • Anees Kazi, S. Arvind krishna, Shayan Shekarforoush, Karsten Kortuem, Shadi Albarqouni, Nassir Navab
A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction.
no code implementations • 27 Sep 2018 • Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni, Sonja Kirchhoff, Alexandra Sträter, Peter Biberthaler, Diana Mateus, Nassir Navab
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system.
no code implementations • 28 Apr 2018 • Anees Kazi, Shadi Albarqouni, Karsten Kortuem, Nassir Navab
Structural data from Electronic Health Records as complementary information to imaging data for disease prediction.
no code implementations • 19 Dec 2016 • Sailesh Conjeti, Anees Kazi, Nassir Navab, Amin Katouzian
This paper presents a new scalable algorithm for cross-modal similarity preserving retrieval in a learnt manifold space.