Juan Felipe Pérez-Juste Abascal, Ph.D.
Researcher in medical imaging, image processing, inverse problems and deep learning
Current position at AMIDS Solutions, Lyon, France
juanabascal78 at gmail.com
Latest projects
Lately, I have had the opportunity to work of the following challenging problems: robust deep iterative reconstruction, high-dimensional image denoising and image restoration, motion artefact reduction, object detection, NLP and ChatGPT prompt text analysis.
Deep high-dimensional denoising
2D+frequency denoising:
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Exploits higher-order convolutions (regularization across both space and frequency)
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Image denoising for single-pixel hyperspectral imaging
Transfer learning
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Learned the detector response function for spectral CT from few datasets
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A Sim2Real transfer learning approach when a ground truth is not available
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Material decomposition of human thorax data
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Improved regularized approaches
Deep residual prior denoising
State-of-the art denoising models
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ResNet, U-Net architecture, ViT
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Specifically designed architectures
Deep learning denoising with Prior Image:
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2D and 3D image denoising of spectral CT images
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Exploits a Prior image (extending sparse and prior-based methods)
Sparse denoising methods
Sparse spatial and prior based denoising (SPADE) method
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3D image denoising
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Exploits a Prior Image
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Improved stability and convergence
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Reduces cartoon-like artefacts
Deep iterative reconstruction
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Robust methods for medical imaging (ensure data fidelity)
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Unrolled methods
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Plug-and-play iterative methods based on state-of-the art denoising
Computer vision
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Video object detection with TFLite on Raspberry Pi
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Intelligent video surveillance
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Image and video restoration
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Semantic segmentation
Photon counting Spectral CT
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EU-H2020 Marie Skłodowska-Curie Project "SUCCESS" on spectral computed tomography (CT)
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Material decomposition for spectral CT
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Regularization methods
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Deep learning
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Feasibility of early detection of Osteoarthritis using spectral CT
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NLP
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Named-entity recognition
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Text classification
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OCR
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ChatGPT prompt text analysis