Search Results for author:
Found 15 papers, 3 papers with code
PINNs for Medical Image Analysis: A Survey
We delve deep into a wide range of image analysis tasks, from imaging, generation, prediction, inverse imaging (super-resolution and reconstruction), registration, and image analysis (segmentation and classification).
SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer-grade Videos
Using this dataset, we assess the accuracy and precision of state-of-the-art methods for 3D reconstruction, ranging from traditional photogrammetry pipelines to advanced neural rendering approaches.
NeRF Director: Revisiting View Selection in Neural Volume Rendering
Neural Rendering representations have significantly contributed to the field of 3D computer vision.
Divide and Conquer: Rethinking the Training Paradigm of Neural Radiance Fields
Neural radiance fields (NeRFs) have exhibited potential in synthesizing high-fidelity views of 3D scenes but the standard training paradigm of NeRF presupposes an equal importance for each image in the training set.
Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis
Wound management poses a significant challenge, particularly for bedridden patients and the elderly.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels
The early stopping strategy averts updating CNNs during the early training phase and is widely employed in the presence of noisy labels.
CorticalFlow$^{++}$: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability
Using the state-of-the-art CorticalFlow model as a blueprint, this paper proposes three modifications to improve its accuracy and interoperability with existing surface analysis tools, while not sacrificing its fast inference time and low GPU memory consumption.
CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction
In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.
Learning Dense Correspondence from Synthetic Environments
Results show that training 2D-3D mapping network models on synthetic data is a viable alternative to using real data.
CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction
In this paper, we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.
MongeNet: Efficient Sampler for Geometric Deep Learning
Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes.
DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction
Having these limitations in mind, we propose DeepCSR, a 3D deep learning framework for cortical surface reconstruction from MRI.
Going deeper with brain morphometry using neural networks
Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases.
A Multiple Decoder CNN for Inverse Consistent 3D Image Registration
The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts.
