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researcher

F. Yin

4 papers hereh-index 556 citations16 works total

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

author position
  • middle author1
  • last author3

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

fields
  • physics.med-ph3
  • eess.IV1
same name
  • F. Yin — 2 papers, h 32
  • F. Yin — 1 paper, h 2
  • F. Yin — 1 paper, h 1

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

physics.med-ph2025

A Dual Radiomic and Dosiomic Filtering Technique for Locoregional Radiation Pneumonitis Prediction in Breast Cancer Patients

Zhenyu Yang, Qian Chen, Rihui Zhang +8

Purpose: Radiation pneumonitis (RP) is a serious complication of intensity-modulated radiation therapy (IMRT) for breast cancer patients, underscoring the need for precise and expl…

physics.med-ph2025

A Voxel-Wise Uncertainty-Guided Framework for Glioma Segmentation Using Spherical Projection-Based U-Net and Localized Refinement in Multi-Parametric MRI

Zhenyu Yang, Chen Yang, Rihui Zhang +3

Purpose: Accurate segmentation of glioma subregions in multi-parametric MRI (MP-MRI) is essential for diagnosis and treatment planning but remains challenging due to tumor heteroge…

physics.med-ph2025

Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification

Zhenyu Yang, Haiming Zhu, Rihui Zhang +5

Background: Deep learning has significantly advanced medical image analysis, with Vision Transformers (ViTs) offering a powerful alternative to convolutional models by modeling lon…

eess.IV2025

Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation

Tianhao Li, Tianyu Zeng, Yujia Zheng +6

Deep learning-based medical image segmentation models, such as U-Net, rely on high-quality annotated datasets to achieve accurate predictions. However, the increasing use of genera…

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