Walter H. L. Pinaya

Orcid: 0000-0003-3739-1087

According to our database1, Walter H. L. Pinaya authored at least 29 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models.
CoRR, 2024

2023
Latent Transformer Models for out-of-distribution detection.
Medical Image Anal., December, 2023

Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation.
CoRR, 2023

Evaluating the structure of cognitive tasks with transfer learning.
CoRR, 2023

Generative AI for Medical Imaging: extending the MONAI Framework.
CoRR, 2023

Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models.
CoRR, 2023

Sleep-Energy: An Energy Optimization Method to Sleep Stage Scoring.
IEEE Access, 2023

InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Geometry-Invariant Abnormality Detection.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Unsupervised 3D Out-of-Distribution Detection with Latent Diffusion Models.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Privacy Distillation: Reducing Re-identification Risk of Diffusion Models.
Proceedings of the Deep Generative Models - Third MICCAI Workshop, 2023

A 3D Generative Model of Pathological Multi-modal MR Images and Segmentations.
Proceedings of the Deep Generative Models - Third MICCAI Workshop, 2023

Self-Supervised Anomaly Detection from Anomalous Training Data via Iterative Latent Token Masking.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Applying Independent Vector Analysis on EEG-Based Motor Imagery Classification.
Proceedings of the IEEE International Conference on Acoustics, 2023

Denoising diffusion models for out-of-distribution detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Unsupervised brain imaging 3D anomaly detection and segmentation with transformers.
Medical Image Anal., 2022

Transformer-based normative modelling for anomaly detection of early schizophrenia.
CoRR, 2022

Transformer-based out-of-distribution detection for clinically safe segmentation.
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain.
Proceedings of the Simulation and Synthesis in Medical Imaging, 2022

Brain Imaging Generation with Latent Diffusion Models.
Proceedings of the Deep Generative Models - Second MICCAI Workshop, 2022

Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection.
Proceedings of the Deep Generative Models - Second MICCAI Workshop, 2022

Can Segmentation Models Be Trained with Fully Synthetically Generated Data?
Proceedings of the Simulation and Synthesis in Medical Imaging, 2022

2021
Estimating Gender and Age from Brain Structural MRI of Children and Adolescents: A 3D Convolutional Neural Network Multitask Learning Model.
Comput. Intell. Neurosci., 2021

Unsupervised Brain Anomaly Detection and Segmentation with Transformers.
Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

2020
Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners.
NeuroImage, 2020

2019
Regional Dynamics of the Resting Brain in Amyotrophic Lateral Sclerosis Using Fractional Amplitude of Low-Frequency Fluctuations and Regional Homogeneity Analyses.
Brain Connect., 2019

2015
Comparing Methods for Determining Motor-Hand Lateralization Based on fTCD Signals.
J. Medical Syst., 2015

2013
Towards an EEG-based biomarker for Alzheimer's disease: Improving amplitude modulation analysis features.
Proceedings of the IEEE International Conference on Acoustics, 2013


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