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T. Tan

4 papers hereh-index 5126 citations28 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • eess.IV1
same name
  • T. Tan — 21 papers, h 20
  • T. Tan — 7 papers, h 32
  • T. Tan — 3 papers, h 6
  • T. Tan — 3 papers, h 13
  • T. Tan — 2 papers, h 1
  • T. Tan — 2 papers, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

cs.CV2026

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…

cs.CV2026

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…

eess.IV2025

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…

cs.CV2025

ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis

Yaofei Duan, Yuhao Huang, Xin Yang +9

Deep learning-based diagnostic models often suffer performance drops due to distribution shifts between training (source) and test (target) domains. Collecting and labeling suffici…

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