Search Results for author: Amir Alansary

Found 21 papers, 11 papers with code

Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption

no code implementations27 Feb 2022 George-Liviu Pereteanu, Amir Alansary, Jonathan Passerat-Palmbach

This work presents a novel protocol for fast secure inference of neural networks applied to computer vision applications.

Model extraction

CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs

1 code implementation16 Feb 2022 Qiang Ma, Liu Li, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary

Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively.

Surface Reconstruction

FedRAD: Federated Robust Adaptive Distillation

no code implementations2 Dec 2021 Stefán Páll Sturluson, Samuel Trew, Luis Muñoz-González, Matei Grama, Jonathan Passerat-Palmbach, Daniel Rueckert, Amir Alansary

The robustness of federated learning (FL) is vital for the distributed training of an accurate global model that is shared among large number of clients.

Federated Learning Knowledge Distillation +1

PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction

1 code implementation6 Sep 2021 Qiang Ma, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary

Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI).

Surface Reconstruction

Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare

no code implementations17 Sep 2020 Matei Grama, Maria Musat, Luis Muñoz-González, Jonathan Passerat-Palmbach, Daniel Rueckert, Amir Alansary

In this work, we implement and evaluate different robust aggregation methods in FL applied to healthcare data.

Cryptography and Security

Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface

1 code implementation13 Aug 2020 Vitalis Vosylius, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loic Le Folgoc, John Cupitt, Antonios Makropoulos, Andreas Schuh, Daniel Rueckert, Amir Alansary

In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface.

Flexible Conditional Image Generation of Missing Data with Learned Mental Maps

no code implementations29 Aug 2019 Benjamin Hou, Athanasios Vlontzos, Amir Alansary, Daniel Rueckert, Bernhard Kainz

Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic.

Anatomy Conditional Image Generation +2

Multiple Landmark Detection using Multi-Agent Reinforcement Learning

1 code implementation30 Jun 2019 Athanasios Vlontzos, Amir Alansary, Konstantinos Kamnitsas, Daniel Rueckert, Bernhard Kainz

We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naive approach of training K agents separately.

Anatomy Multi-agent Reinforcement Learning +2

Fast Multiple Landmark Localisation Using a Patch-based Iterative Network

1 code implementation18 Jun 2018 Yuanwei Li, Amir Alansary, Juan J. Cerrolaza, Bishesh Khanal, Matthew Sinclair, Jacqueline Matthew, Chandni Gupta, Caroline Knight, Bernhard Kainz, Daniel Rueckert

PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume.

Multi-Task Learning

3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images

no code implementations19 Sep 2017 Benjamin Hou, Bishesh Khanal, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz

We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline.

3D Reconstruction Image Reconstruction +2

Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion

1 code implementation28 Feb 2017 Benjamin Hou, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz

Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data.

Image Registration Motion Compensation +1

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