:: Library Catalog

Ata uhi

I tiakina i:

Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Atalık, Arda, Chopra, Sumit, Sodickson, Daniel K.
Hōputu:	Preprint
I whakaputaina:	2025
Ngā marau:	Image and Video Processing Computer Vision and Pattern Recognition Machine Learning
Urunga tuihono:	https://arxiv.org/abs/2501.03021
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Ngā tūemi rite

PSIRNet: Deep Learning-based Free-breathing Rapid Acquisition Late Enhancement Imaging
mā: Atalik, Arda, me ētahi atu.
I whakaputaina: (2026)

HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal Automatic Diagnosis
mā: Huang, Haoxu, me ētahi atu.
I whakaputaina: (2024)

Optimisation of SOUP-GAN and CSR-GAN for High Resolution MR Images Reconstruction
mā: Rashid, Muneeba, me ētahi atu.
I whakaputaina: (2026)

Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
mā: Alsubaie, Mohammed, me ētahi atu.
I whakaputaina: (2025)

Atlas-Based Interpretable Age Prediction In Whole-Body MR Images
mā: Starck, Sophie, me ētahi atu.
I whakaputaina: (2023)

Estimating Head Motion from MR-Images
mā: Pollak, Clemens, me ētahi atu.
I whakaputaina: (2023)

Motion-Informed Deep Learning for Brain MR Image Reconstruction Framework
mā: Chen, Zhifeng, me ētahi atu.
I whakaputaina: (2024)

Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Images
mā: Zhang, Yundi, me ētahi atu.
I whakaputaina: (2024)

Deep Learning Superresolution for 7T Knee MR Imaging: Impact on Image Quality and Diagnostic Performance
mā: Chen, Pinzhen, me ētahi atu.
I whakaputaina: (2026)

Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimation
mā: Pan, Jiazhen, me ētahi atu.
I whakaputaina: (2023)

Attention-aware non-rigid image registration for accelerated MR imaging
mā: Ghoul, Aya, me ētahi atu.
I whakaputaina: (2024)

Pediatric TSC-Related Epilepsy Classification from Clinical MR Images Using Quantum Neural Network
mā: Lin, Ling, me ētahi atu.
I whakaputaina: (2024)

WALINET: A water and lipid identification convolutional Neural Network for nuisance signal removal in 1H MR Spectroscopic Imaging
mā: Weiser, Paul, me ētahi atu.
I whakaputaina: (2024)

Image-Adaptive GAN based Reconstruction
mā: Hussein, Shady Abu, me ētahi atu.
I whakaputaina: (2019)

Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images
mā: Sahayam, Subin, me ētahi atu.
I whakaputaina: (2024)

Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images
mā: Sahayam, Subin, me ētahi atu.
I whakaputaina: (2024)

Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation
mā: Lyu, Jun, me ētahi atu.
I whakaputaina: (2025)

Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction
mā: Cheung, Matt Y, me ētahi atu.
I whakaputaina: (2024)

EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation
mā: Körber, Nikolai, me ētahi atu.
I whakaputaina: (2023)

Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
mā: Behrendt, Finn, me ētahi atu.
I whakaputaina: (2023)

HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling
mā: Duenias, Daniel, me ētahi atu.
I whakaputaina: (2024)

Iteratively Refined Image Reconstruction with Learned Attentive Regularizers
mā: Pourya, Mehrsa, me ētahi atu.
I whakaputaina: (2024)

DEALing with Image Reconstruction: Deep Attentive Least Squares
mā: Pourya, Mehrsa, me ētahi atu.
I whakaputaina: (2025)

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation
mā: Pali, Marie-Christine, me ētahi atu.
I whakaputaina: (2025)

NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images
mā: Li, Weiming, me ētahi atu.
I whakaputaina: (2023)

An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation
mā: Ren, Tianyi, me ētahi atu.
I whakaputaina: (2024)

Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
mā: Zhang, Ling-Qi, me ētahi atu.
I whakaputaina: (2024)

PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging
mā: Cai, Xin, me ētahi atu.
I whakaputaina: (2024)

Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models
mā: Akbar, Muhammad Usman, me ētahi atu.
I whakaputaina: (2023)

Adaptive Guided Upsampling for Low-light Image Enhancement
mā: Dcosta, Angela Vivian, me ētahi atu.
I whakaputaina: (2025)

LadleNet: A Two-Stage UNet for Infrared Image to Visible Image Translation Guided by Semantic Segmentation
mā: Zou, Tonghui, me ētahi atu.
I whakaputaina: (2023)

Image2Points:A 3D Point-based Context Clusters GAN for High-Quality PET Image Reconstruction
mā: Cui, Jiaqi, me ētahi atu.
I whakaputaina: (2024)

Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising
mā: Yoon, Siyeop, me ētahi atu.
I whakaputaina: (2024)

Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction
mā: Bell, Evan, me ētahi atu.
I whakaputaina: (2025)

Frequency-Guided Posterior Sampling for Diffusion-Based Image Restoration
mā: Thaker, Darshan, me ētahi atu.
I whakaputaina: (2024)

Reconstructing Depth Images of Moving Objects from Wi-Fi CSI Data
mā: Cao, Guanyu, me ētahi atu.
I whakaputaina: (2025)

Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet
mā: Pandey, Sumit, me ētahi atu.
I whakaputaina: (2024)

RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images
mā: Li, Guang, me ētahi atu.
I whakaputaina: (2022)

An Attention-Guided Deep Learning Approach for Classifying 39 Skin Lesion Types
mā: Hanum, Sauda Adiv, me ētahi atu.
I whakaputaina: (2025)

Multivariate Fields of Experts for Convergent Image Reconstruction
mā: Ducotterd, Stanislas, me ētahi atu.
I whakaputaina: (2025)