Meet ApplyKite Twin, the application assistant that learns with you

Meet your Twin, it learns with you

Your Twin learns with you

Launch week codes:30% off annual Pro20% off monthly Pro

Ends in00h00m00s

Su Ruan

University of Rouen

Research Interests

Explore related searches

Articles (23)

Collaborators (5)

Baiying Lei

Shenzhen University

Mengling Feng

National University of Singapore (NUS)

Jinming Duan

University of Birmingham

UNITED KINGDOM

Thierry Denoeux

Université de Technologie de Compiègne

Ling Huang

National University of Singapore

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Su Ruan

Research Interests

Explore related searches

Articles (23)

Collaborators (5)

Baiying Lei

Mengling Feng

Jinming Duan

Thierry Denoeux

Ling Huang

Make your next move.

Su Ruan

Research Interests

Explore related searches

Articles (23)

ASMFNet: Anatomical symmetry-guided multi-modal fusion network for glioma segmentation in MRI

FRMF-Net: Feature rectification and adaptive modality fusion guided multi-modal brain tumor segmentation network

UTriGate-Net : Uncertainty-aware brain tumor segmentation via triaxial context encoding and gated modality fusion

Artificial intelligence in microscopic hair imaging for scalp disorders: From image acquisition to clinical decisions

A hierarchical teacher-student learning framework with adaptive cross-modal fusion for brain tumor segmentation

A reliable framework for brain tumor segmentation via multi-modal fusion and uncertainty modeling

Global and local Mamba network for multi-modality medical image super-resolution

DFuse-Net: Disentangled feature fusion with uncertainty-aware learning for reliable multi-modal brain tumor segmentation

BUFNet: Boundary-aware and uncertainty-driven multi-modal fusion network for MR brain tumor segmentation

EsurvFusion: An evidential multimodal survival fusion model based on Epistemic random fuzzy sets

Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation

Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation

Iterative Collaboration Network Guided by Reconstruction Prior for Medical Image Super-Resolution

Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review

Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images

Multi-task multi-scale learning for outcome prediction in 3D PET images

Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation

Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction

Correction: Brochet et al. A Quantitative Comparison between Shannon and Tsallis–Havrda–Charvat Entropies Applied to Cancer Outcome Prediction. <i>Entropy</i> 2022, <i>24</i>, 436

A Quantitative Comparison between Shannon and Tsallis&ndash;Havrda&ndash;Charvat Entropies Applied to Cancer Outcome Prediction

Learning-Based Cancer Treatment Outcome Prognosis Using Multimodal Biomarkers

Segmentation of multicorrelated images with copula models and conditionally random fields

Automatic COVID‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism

Collaborators (5)

Baiying Lei

Mengling Feng

Jinming Duan

Thierry Denoeux

Ling Huang

Make your next move.

A Quantitative Comparison between Shannon and Tsallis–Havrda–Charvat Entropies Applied to Cancer Outcome Prediction