Search Results for author:
Found 21 papers, 11 papers with code
CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development.
Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption
This work presents a novel protocol for fast secure inference of neural networks applied to computer vision applications.
CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs
Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively.
FedRAD: Federated Robust Adaptive Distillation
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.
PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction
Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI).
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
This mismatch is known as sampling bias.
Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare
In this work, we implement and evaluate different robust aggregation methods in FL applied to healthcare data.
Cryptography and Security
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images
Accurate detection of anatomical landmarks is an essential step in several medical imaging tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface
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.
A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling
We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI.
Flexible Conditional Image Generation of Missing Data with Learned Mental Maps
Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic.
Multiple Landmark Detection using Multi-Agent Reinforcement Learning
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.
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes.
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network
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.
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Navigating through target anatomy to find the required view plane is tedious and operator-dependent.
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
In this paper, we propose a general Riemannian formulation of the pose estimation problem.
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images
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.
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging
Fast imaging is required for targets that move to avoid motion artefacts.
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data.
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus.
Learning under Distributed Weak Supervision
The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods.
