Search Results for author: Seyed-Ahmad Ahmadi

Found 17 papers, 5 papers with code

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

1 code implementation14 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).

Classification Disease Prediction +3

Decision Support for Intoxication Prediction Using Graph Convolutional Networks

no code implementations2 May 2020 Hendrik Burwinkel, Matthias Keicher, David Bani-Harouni, Tobias Zellner, Florian Eyer, Nassir Navab, Seyed-Ahmad Ahmadi

Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame.

Latent Patient Network Learning for Automatic Diagnosis

no code implementations27 Mar 2020 Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein

Recently, Graph Convolutional Networks (GCNs) has proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction.

Disease Prediction General Classification +1

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

no code implementations8 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.

Classification Decision Making +1

Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks

no code implementations8 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.

Classification General Classification

Stabilizing Inputs to Approximated Nonlinear Functions for Inference with Homomorphic Encryption in Deep Neural Networks

no code implementations5 Feb 2019 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 inference with Deep Neural Networks (DNN).

Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion

no code implementations30 Mar 2018 Gerome Vivar, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi

In this work, we follow up on the idea of modeling multi-modal disease classification as a matrix completion problem, with simultaneous classification and non-linear imputation of features.

Classification General Classification +2

TOMAAT: volumetric medical image analysis as a cloud service

no code implementations19 Mar 2018 Fausto Milletari, Johann Frei, Seyed-Ahmad Ahmadi

Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems.

Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning

no code implementations26 Jan 2018 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-dimensional space.

General Classification

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

15 code implementations15 Jun 2016 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.

Volumetric Medical Image Segmentation

Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound

no code implementations26 Jan 2016 Fausto Milletari, Seyed-Ahmad Ahmadi, Christine Kroll, Annika Plate, Verena Rozanski, Juliana Maiostre, Johannes Levin, Olaf Dietrich, Birgit Ertl-Wagner, Kai Bötzel, Nassir Navab

In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs).

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