Xiaoman Zhang
副教授
Works on AI for life sciences, including virtual cell modeling, target discovery and drug development.
Xiaoman Zhang's lab at SJTU works on proteins, drugs & materials and genomics & computational biology. 32 papers since 2023, 11 at top venues, mostly Nature Communications and CVPR. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 34%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 8 papers2026 · T1 1 · collecting
- 14 papers2025 · T1 2
- 6 papers2024 · T1 5
- 4 papers2023 · T1 3
T1 venuesOtherVenues: Nature Communications, CVPR, ECCV, EMNLP, ICCV
Research focuspaper titles that use each topic's words
- Genomics & computational biology1 paper
- Proteins, drugs & materials0 papers
- target0 papers
- life0 papers
- cell0 papers
Paper list
From the lab homepage.
20268 papers
- PSB3DReasonKnee: Advancing Grounded Reasoning in Medical Vision Language Models
- Nature MedicineA Clinical Environment Simulator for Dynamic AI Evaluation
- Nature (T1)An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
- PSBAutomated Chest X-ray Report Generation Remains Unsolved
- PSBED-Explain: Personalized Video Instructions for Patients Discharged from the Emergency Department
- JACCOpportunistic Cardiovascular Risk Assessment Using Routine Head CT in the Emergency Department
- MIDLReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challenges
- CHILReXSonoVQA: A Video QA Benchmark for Procedure-Centric Ultrasound Understanding
202514 papers
- CVPR (T1)FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models
- CHILHeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation
- NEJM AIInternational Retrospective Observational Study of Continual Learning for AI on Endotracheal Tube Placement from Chest Radiographs
- JBHIInterpretable Brain MRI Report Generation Anchored by Lesion Topography
- npj Digital MedicineLarge-Vocabulary Segmentation for Medical Images with Text Prompts
- Nature Scientific DataRadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis
- ML4HReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-Text Reports
- AAAI Bridge Program - AIMedHealthReXplain: Translating Radiology into Patient-Friendly Video Reports
- AAAI Bridge Program - AIMedHealthReXrank: A Public Leaderboard for AI-Powered Radiology Report Generations
- ML4HState-of-the-Art Text-Prompted Medical Segmentation Models Struggle to Ground Chest CT Findings
- RadiologyThe Financial, Operational, and Clinical Advantages of Generalist Radiology AI
- Nature Communications (T1)Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D & 3D Medical Data
20246 papers
- ECCV (T1)Knowledge-enhanced Visual-Language Pretraining for Computational Pathology
- Nature Communications (T1)Large-Scale Long-Tailed Disease Diagnosis on Radiology Images
- JAMIA (T1)PMC-LLaMA: Towards Building Open-source Language Models for Medicine
- Communications MedicinePMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
- EMNLP (T1)RaTEScore: A Metric for Radiology Report Generation
- Nature Communications (T1)Towards Building Multilingual Language Model for Medicine
20234 papers
- Nature Communications (T1)Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images
- ICCV (T1)MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training for X-ray Diagnosis
- MICCAI (T1)PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents
- JBHISelf-supervised Tumor Segmentation with Sim2Real Adaptation
Where alumni went
Alumni are not listed on the lab homepage.
Apply to SJTUFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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