End-to-end reconstruction-classification learning for face forgery detection
Existing face forgery detectors mainly focus on specific forgery patterns like noise
characteristics, local textures, or frequency statistics for forgery detection. This causes …
characteristics, local textures, or frequency statistics for forgery detection. This causes …
Deep learning-based reconstruction for cardiac MRI: a review
Cardiac magnetic resonance (CMR) is an essential clinical tool for the assessment of
cardiovascular disease. Deep learning (DL) has recently revolutionized the field through …
cardiovascular disease. Deep learning (DL) has recently revolutionized the field through …
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging
Physics-driven deep learning methods have emerged as a powerful tool for computational
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …
A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies
Accelerated magnetic resonance imaging (MRI) has played an essential role in reducing
data acquisition time for MRI. Acceleration can be achieved by acquiring fewer data points in …
data acquisition time for MRI. Acceleration can be achieved by acquiring fewer data points in …
RLGC: Reconstruction learning fusing gradient and content features for efficient deepfake detection
Current deepfake detection methods, which utilize noise features, localized textures, or
frequency statistics, may perform well in special domains or forgery methods. But the …
frequency statistics, may perform well in special domains or forgery methods. But the …
Self‐supervised learning for improved calibrationless radial MRI with NLINV‐Net
M Blumenthal, C Fantinato… - Magnetic …, 2024 - Wiley Online Library
Purpose To develop a neural network architecture for improved calibrationless
reconstruction of radial data when no ground truth is available for training. Methods NLINV …
reconstruction of radial data when no ground truth is available for training. Methods NLINV …
Towards Unified Defense for Face Forgery and Spoofing Attacks via Dual Space Reconstruction Learning
Real-world face recognition systems are vulnerable to diverse face attacks, ranging from
digitally manipulated artifacts to physically crafted spoofing attacks. Existing works primarily …
digitally manipulated artifacts to physically crafted spoofing attacks. Existing works primarily …
Multi‐mask self‐supervised learning for physics‐guided neural networks in highly accelerated magnetic resonance imaging
Self‐supervised learning has shown great promise because of its ability to train deep
learning (DL) magnetic resonance imaging (MRI) reconstruction methods without fully …
learning (DL) magnetic resonance imaging (MRI) reconstruction methods without fully …
[PDF][PDF] Physics-driven deep learning for computational magnetic resonance imaging
Physics-driven deep learning methods have emerged as a powerful tool for computational
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …
High‐resolution spiral real‐time cardiac cine imaging with deep learning‐based rapid image reconstruction and quantification
The objective of the current study was to develop and evaluate a DEep learning‐based
rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)‐based segmentation …
rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)‐based segmentation …