8 papers
Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V
Xinglong Liang, Chunyao Lu, Tianyu Zhang +4
Interactive lesion segmentation in whole-body PET/CT requires a model to provide a strong initial prediction while also responding efficiently to sparse corrective scribbles during…
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…
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…
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…
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…