# Yichi Zhang
**Type:** Researchers
**Canonical URL:** https://scholariq.org/researchers/yichi-zhang/
## Facts
| Field | Value |
| --- | --- |
| Citations | 3,541 |
| Field | Radiomics and Machine Learning in Medical Imaging |
| h-index | 24 |
| i10-index | 48 |
| Last Known Institution | National University of Singapore |
| OpenAlex ID | https://openalex.org/A5100444188 |
| ORCID iD | 0000-0002-4292-6835 |
| Works | 163 |
## Researcher papers
- [Fabrication of Ag@SiO2@Y2O3:Er Nanostructures for Bioimaging: Tuning of the Upconversion Fluorescence with Silver Nanoparticles](https://scholariq.org/papers/fabrication-of-ag-sio-sub-2-sub-y-sub-2-sub-o-sub-3-sub-er-nanostructures-for/)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?](https://scholariq.org/papers/abdomenct-1k-is-abdominal-organ-segmentation-a-solved-problem/)
- [Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation](https://scholariq.org/papers/learning-with-limited-annotations-a-survey-on-deep-semi-supervised-learning-for/)
- [Segment anything model for medical image segmentation: Current applications and future directions](https://scholariq.org/papers/segment-anything-model-for-medical-image-segmentation-current-applications-and/)
- [How Segment Anything Model (Sam) Boost Medical Image Segmentation: A Survey](https://scholariq.org/papers/how-segment-anything-model-sam-boost-medical-image-segmentation-a-survey/)
- [Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data](https://scholariq.org/papers/deep-learning-for-differential-diagnosis-of-malignant-hepatic-tumors-based-on/)
- [Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions](https://scholariq.org/papers/bridging-2d-and-3d-segmentation-networks-for-computation-efficient-volumetric/)
- [Uncertainty-guided mutual consistency learning for semi-supervised medical image segmentation](https://scholariq.org/papers/uncertainty-guided-mutual-consistency-learning-for-semi-supervised-medical-image/)
- [Enzyme-Responsive Peptide Thioesters for Targeting Golgi Apparatus](https://scholariq.org/papers/enzyme-responsive-peptide-thioesters-for-targeting-golgi-apparatus/)
- [Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation](https://scholariq.org/papers/mamba-unet-unet-like-pure-visual-mamba-for-medical-image-segmentation/)
- [Transformer-Based Multi-modal Fusion for Automatic Recognition and Learning of Craniomaxillofacial Suturing Skills](https://scholariq.org/papers/transformer-based-multi-modal-fusion-for-automatic-recognition-and-learning-of/)
## Researcher topics
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [Medical Image Segmentation Techniques](https://scholariq.org/topics/medical-image-segmentation-techniques/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)
- [Advanced Graph Neural Networks](https://scholariq.org/topics/advanced-graph-neural-networks/)
## Researcher university
- [National University of Singapore](https://scholariq.org/institutions/national-university-of-singapore/)
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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.