4 citations · 4 across the 13 of their papers we have counts for
13 papers
ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction
Yaofei Duan, Yuhao Huang, Tianyu Zhang +12
Neoadjuvant chemotherapy (NAC) response prediction is clinically important for treatment stratification in breast cancer. However, robust pre-treatment pathological complete respon…
SAFE-Diff: Scale-Aware Attention and Feature-Dispersive Diffusion with Uncertainty Estimation for Contrast-Enhanced Breast MRI Synthesis
Tianyu Zhang, Xinglong Liang, Jarek van Dijk +13
Synthesizing high fidelity contrast enhanced MRI is clinically valuable for safer and more efficient breast cancer screening, yet remains challenging due to complex lesion textures…
LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling
Xin Wang, Yuan Gao, George Yiasemis +13
Efficient and explainable breast cancer (BC) risk prediction is critical for large-scale population-based screening. Breast MRI provides functional information for personalized ris…
DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation
Xinglong Liang, Jiaju Huang, Luyi Han +7
PET-CT lesion segmentation is challenging due to noise sensitivity, small and variable lesion morphology, and interference from physiological high-metabolic signals. Current mainst…
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey
Hao Yang, Xinlong Liang, Zhang Li +12
Deep learning offers transformative potential in medical imaging, yet its clinical adoption is frequently hampered by challenges such as data scarcity, distribution shifts, and the…
crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
Navodini Wijethilake, Reuben Dorent, Marina Ivory +38
The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assist…
Foundation Models in Medical Imaging: A Review and Outlook
Vivien van Veldhuizen, Vanessa Botha, Chunyao Lu +10
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FM…
A European Multi-Center Breast Cancer MRI Dataset
Gustav Müller-Franzes, Lorena Escudero Sánchez, Nicholas Payne +18
Early detection of breast cancer is critical for improving patient outcomes. While mammography remains the primary screening modality, magnetic resonance imaging (MRI) is increasin…
Comparing DWI image quality of deep-learning-reconstructed EPI with RESOLVE in breast lesions at 3.0T: a pilot study
Marialena I. Tsarouchi, Antonio Portaluri, Marnix Maas +1
The challenging spatial resolution of DWI could be addressed by deep learning based image reconstruction, by reducing noise without increasing acquisition time. To compare the imag…
Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms
Xin Wang, Tao Tan, Yuan Gao +9
Precision breast cancer (BC) risk assessment is crucial for developing individualized screening and prevention. Despite the promising potential of recent mammogram (MG) based deep…
Non-Adversarial Learning: Vector-Quantized Common Latent Space for Multi-Sequence MRI
Luyi Han, Tao Tan, Tianyu Zhang +7
Adversarial learning helps generative models translate MRI from source to target sequence when lacking paired samples. However, implementing MRI synthesis with adversarial learning…
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
Luyi Han, Tao Tan, Tianyu Zhang +7
Clinicians compare breast DCE-MRI after neoadjuvant chemotherapy (NAC) with pre-treatment scans to evaluate the response to NAC. Clinical evidence supports that accurate longitudin…
Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation
Luyi Han, Tao Tan, Ritse Mann
The domain adaptation approach has gained significant acceptance in transferring styles across various vendors and centers, along with filling the gaps in modalities. However, mult…