Yuerong Lizhu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yuerong Lizhu has a total of 136 co-authors. Here are the top co-authors by number of shared publications:
Basheng Hu
Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
1 shared publicationCheng Xu
Department of Imaging, Shanxi Provincial People’s Hospital, Taiyuan, China
1 shared publication- Ching-Po Lin
Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
1 shared publication - Chu-Chung Huang
School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
1 shared publication Chunjie Guo
Department of Radiology, The First Hospital of Jilin University, Changchun, China
1 shared publicationDan Cheng
Department of Radiology, University of Washington School of Medicine, Seattle, USA
1 shared publication
Latest Publications
- Open Access
- 3 November 2026
- Published by Springer Nature in Nature Communications
Mapping hippocampal subfield heterogeneity across neurological disorders by normative modeling
- 3 November 2026
- Published by Springer Nature in Nature Communications
Open AccessMapping hippocampal subfield heterogeneity across neurological disorders by normative modeling
- 6 November 2025
- Published by Springer Nature in Nature Biomedical Engineering
- Vol. 10 (8) , 1-19
A vision–language pretrained transformer for versatile clinical respiratory disease applications
General artificial intelligence models have unique challenges in clinical practice when applied to diverse modalities and complex clinical tasks. Here we present MedMPT, a versatile, clinically oriented pretrained model tailored for respiratory healthcare, trained on 154,274 pairs of chest computed-tomography scans and radiograph reports. MedMPT adopts self-supervised learning to acquire medical insights and is capable of handling multimodal clinical data and supporting various clinical tasks aligned with clinical workflows. We evaluate the performance of MedMPT on a broad spectrum of chest-related pathological conditions, involving common medical modalities such as computed tomography images, radiology reports, laboratory tests and drug relationship graphs. MedMPT consistently outperforms the state-of-the-art multimodal pretrained models in the medical domain, achieving significant improvements in diverse clinical tasks. Extensive analysis indicates that MedMPT effectively harnesses the potential of medical data, showing both data and parameter efficiency and offering explainable insights for decision-making. MedMPT highlights the potential of multimodal pretrained models in the realm of general-purpose artificial intelligence for clinical practice.
- 6 November 2025
- Published by Springer Nature in Nature Biomedical Engineering
- Vol. 10 (8) , 1-19
A vision–language pretrained transformer for versatile clinical respiratory disease applications
General artificial intelligence models have unique challenges in clinical practice when applied to diverse modalities and complex clinical tasks. Here we present MedMPT, a versatile, clinically oriented pretrained model tailored for respiratory healthcare, trained on 154,274 pairs of chest computed-tomography scans and radiograph reports. MedMPT adopts self-supervised learning to acquire medical insights and is capable of handling multimodal clinical data and supporting various clinical tasks aligned with clinical workflows. We evaluate the performance of MedMPT on a broad spectrum of chest-related pathological conditions, involving common medical modalities such as computed tomography images, radiology reports, laboratory tests and drug relationship graphs. MedMPT consistently outperforms the state-of-the-art multimodal pretrained models in the medical domain, achieving significant improvements in diverse clinical tasks. Extensive analysis indicates that MedMPT effectively harnesses the potential of medical data, showing both data and parameter efficiency and offering explainable insights for decision-making. MedMPT highlights the potential of multimodal pretrained models in the realm of general-purpose artificial intelligence for clinical practice.