Skip To Main Content
Yinghua Zhao on Muck Rack

Yinghua Zhao

  • Editor in Chief, Beijing Science and Technology News
As seen in: British Journal of Cancer, Nature, Wiley Online Library, BioRxiv, EClinicalMedicine, Canadian Journal of Infectious Diseases & Medical Microbiology
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Yinghua

Contact Yinghua, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

An explainable biomedical foundation model via large-scale concept-enhanced vision–language pretraining

|
By Yuxiang Nie, Sunan He, Yihui Wang, Zhixuan Chen, Shu Yang, Zhiyuan Cai, Linshan Wu, Hongmei Wang, Xi Wang, Luyang Luo, Mingxiang Wu, Haibo Jin, Xian Tong Wu, Yuk Ming Lau, Zhengyu Zhang, Sushan Xiao, Yinghua Zhao, Xiaohui Duan, Li Zhang, Yefeng Zheng, Pranav Rajpurkar, Hao Chen, Yequan Bie
| Nature Verified
Abstract Artificial intelligence for medical imaging is required to be accurate and interpretable to clinicians. However, current multimodal biomedical foundation models often prioritize performance over explainability. Here we present ConceptCLIP, an explainable biomedical foundation model that achieves state-of-the-art diagnostic accuracy while delivering human-interpretable explanations across diverse imaging modalities.
Open in Who Shared Issue with article?

Large-scale multi-sequence pretraining for generalizable MRI analysis in versatile clinical applications

|
By Zelin Qiu, Xi Wang, Zhuoyao Xie, Juan Zhou, Yu Wang, Lingjie Yang, Xinrui Jiang, Juyoung Bae, Qiang Ye, Dexuan Chen, Rui Zhang, Tao Li, Neeraj Mahboobani, Varut Vardhanabhuti, Xiaohui Duan, Yinghua Zhao, Hao Chen, Moo Hyun Son
| Nature Verified
Abstract Multi-sequence magnetic resonance imaging (MRI) is essential for clinical diagnosis because it enables comprehensive characterization of complex anatomy. However, its substantial heterogeneity limits the generalizability of deep learning models and hinders clinical translation.
Open in Who Shared Issue with article?

A universal foundation model for grounded biomedical image interpretation

|
By Linshan Wu, Yuxiang Nie, Sunan He, Jiaxin Zhuang, Luyang Luo, Tao Li, Zhuoyao Xie, Dexuan Chen, Yinghua Zhao, Varut Vardhanabhuti, Yifan Peng, Pranav Rajpurkar, Hao Chen, Neeraj Mahboobani
| Nature Verified
Abstract The integration of AI-assisted biomedical image analysis into clinical practice demands AI-generated findings that are not only accurate but also interpretable. However, existing models generally lack the ability to simultaneously generate diagnostic findings and localize corresponding targets. This limitation makes it challenging to correlate AI-generated findings with visual evidence and interpret the results.
Open in Who Shared Issue with article?
See all 10 licensed articles (12 Total)