Muzhen He

Integrated model of MRI biomarkers and clinical factors for predicting pathologic response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Zijie Lin, Muzhen He, Weixiong Xiao, Wei Zhang et autres

cn (code pays fourni par la source)

MALCNet: modality-aware latent completion network for brain tumor segmentation with incomplete MRI modalities

Mingzhe Zhang, Huijian Chen, Yang Song, YuYing Lin et autres

Accurate brain tumor segmentation from multi-modal magnetic resonance imaging (MRI) relies on complementary information from T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR sequences. In clinical practice and retrospective studies, one or more MRI modalities are often missing due to heterogeneous acquisition protocols, patient-related …

cn (code pays fourni par la source)

Interpretable machine-learning survival prediction of breast cancer prognosis from lifestyle factors: evidence from UK biobank

Yishan Yao, Xiaobing Zhai, Zhichao Liang, Chi Kin Lam et autres

mo, cn, gb, nl (code pays fourni par la source)

ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction

Yaofei Duan, Y Huang, Tianyu Zhang, Yuan Gao et autres

Neoadjuvant chemotherapy (NAC) response prediction is clinically important for treatment stratification in breast cancer. However, robust pre-treatment pathological complete response (pCR) prediction remains challenging due to insufficient cross-modal modeling, multicenter imaging heterogeneity, and weak evidence-grounded interpretability. We propose ClinRAG-GRAPH, a Clinically informed …

ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction

Yaofei Duan, Yuhao Huang, Tianyu Zhang, Gao Y et autres

Neoadjuvant chemotherapy (NAC) response prediction is clinically important for treatment stratification in breast cancer. However, robust pre-treatment pathological complete response (pCR) prediction remains challenging due to insufficient cross-modal modeling, multicenter imaging heterogeneity, and weak evidence-grounded interpretability. We propose ClinRAG-GRAPH, a Clinically informed …

us, nl, mo, cn (code pays fourni par la source)

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, Luyi Han et autres

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 and heterogeneous enhancement patterns.

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, Luyi Han et autres

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 and heterogeneous enhancement patterns.

nl, mo (code pays fourni par la source)

Decoding Tumor Phenotypes: A Radiologist-Inspired Deep Learning Framework for Breast Cancer Recurrence Prediction

Tao Tan, Chunyao Lu, Tianyu Zhang, Xinglong Liang et autres

nl, us, cn, jp, mo (code pays fourni par la source)

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling

Xin Wang, Yuan Gao, George Yiasemis, Antonio Portaluri et autres

Efficient and explainable breast cancer (BC) risk prediction is critical for large-scale population-based screening. Breast MRI provides functional information for personalized risk assessment. Yet effective modeling remains challenging as fully 3D CNNs capture volumetric context at high computational cost, whereas lightweight 2D …

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling

Xin Wang, Yuan Gao, George Yiasemis, Antonio Portaluri et autres

Efficient and explainable breast cancer (BC) risk prediction is critical for large-scale population-based screening. Breast MRI provides functional information for personalized risk assessment. Yet effective modeling remains challenging as fully 3D CNNs capture volumetric context at high computational cost, whereas lightweight 2D …

nl, us, mo (code pays fourni par la source)

Impact of Lifestyle Patterns on Breast Cancer Prognosis: Evidence from a UK Biobank Survival Study

Yishan Yao, Xiaobing Zhai, Zhichao Liang, Chi Kin Lam et autres

mo, cn, gb, nl (code pays fourni par la source)

An Interpretable Lesion‐Aware Diagnostic Model for Full‐Field Mammography Classification

Jie Yu, Min Wei, Mingzhe Zhang, Huijian Chen et autres

ABSTRACT Accurate diagnosis of breast cancer is critical for improving patient outcomes. Yet breast lesions are small and mammograms are high‐resolution, patch‐based methods that often ignore peritumoral context, undermining diagnostic accuracy. We therefore develop an interpretable lesion‐aware diagnostic model (LADM), which directly …

cn, us (code pays fourni par la source)