高峰(研究员,博士生导师)

研究方向:本团队聚焦于人工智能与生物医学的前沿交叉领域,致力于利用尖端AI技术深度解析超大规模、多模态(涵盖基因组、转录组、蛋白质组、数字病理、医学影像及临床文本等)生物医学大数据。我们的核心目标是研发下一代精准医学智能系统,旨在贯穿疾病风险预警、早期智能筛查、自动化精准分型、个体化治疗策略生成、实时疗效动态监控及远期生存预后预测的全周期健康管理。我们特别关注如何将AI算法的强大能力转化为临床实践中的可靠工具,赋能医生决策,最终改善患者结局。
主要技术方向包括:
1,可解释人工智能(XAI)方法研发与应用: 重点探索应用于高维、异构生物医学数据(尤其是基因组学数据)的XAI新范式,旨在洞察复杂疾病发生发展的生物学机制,提升模型透明度与临床可信度
2,面向特定疾病的多模态大模型构建: 依托团队构建的PB级数据平台,融合影像、组学、病理、临床等多源信息,构建强大的、具备深度理解与推理能力的疾病特异性基础模型,以驱动精准诊断与治疗的突破。
3,医学影像智能分析与手术辅助技术: 研发先进的计算机视觉与深度学习算法,实现医学影像(CT/MRI/病理等)的高精度自动分割、三维重建、病灶检测与量化分析;并进一步探索基于实时视频流的智能场景理解与增强现实(AR)技术,为外科医生提供智能化的术前规划、术中导航与决策支持
4,大语言模型(LLM)驱动的智慧医疗系统: 利用大语言模型处理非结构化医疗文本(如电子病历、医学文献)的卓越能力,构建智能化的医疗信息快速检索、知识问答以及AI辅助临床决策支持系统,提升医疗信息利用效率和临床工作效能。


课题组主页:fenggaolab.org


个人介绍:中山大学百人计划引进人才,任国家智能社会治理实验专家组成员,上海人工智能实验室顾问科学家、香港科技大学(广州)兼任教授。同时担任多家国际SCI期刊(如CCDT、Life、Frontiers等)的客座主编。作为国际癌症基因组联盟(ICGC-ARGO)肠癌项目数据分析负责人,领导团队建立了涵盖患者全生命周期的PB级大数据及分析平台,整合了高通量组学、数字病理和医学影像等多模态数据。近年来,在Gastroenterology、Hepatology、Medical Image Analysis等顶级国际期刊发表SCI论文50余篇(总影响因子超过500分,影响因子大于10的论文23篇),并开发了DeepCC、HTSanalyzeR2、3D RP-Net等生物医学数据分析工具,已获授权国家发明专利16项,曾获中山大学第六届“芙兰奖”和WILEY威立中国开放科学2022年度作者奖。


代表性论文代表性项目荣誉

1.Interpretable Multimodal Fusion Model for Bridged Histology and Genomics Survival Prediction in Pan-Cancer, Advanced Science, 2025
2.Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis, Genomics Proteomics and Bioinformatics, 2025
3.TMO-Net: An Explainable Pretrained Multi-omics Model for Multi-task Learning in Oncology, Genome Biology, 2024
4.Deciphering Tertiary Lymphoid Structure Heterogeneity Reveals Prognostic Signature and Therapeutic Potentials for Colorectal Cancer: A Multicenter Retrospective Cohort Study, International Journal of Surgery, 2024
5.Senescence‐based Colorectal Cancer Subtyping Reveals Distinct Molecular Characteristics and Therapeutic Strategies, MedComm, 2023
6.A Longitudinal MRI-based Artificial Intelligence System to Predict Pathological Complete Response after Neoadjuvant Therapy in Rectal Cancer: A Multicenter Validation Study, Diseases of the Colon & Rectum, 2023
7.The Growth Pattern of Liver Metastases on MRI Predicts Early Recurrence in Patients with Colorectal Cancer: A Multicenter Study, European Radiology, 2022
8.Segmentation Only Uses Sparse Annotations: Unified Weakly and Semi-supervised Learning in Medical Images, Medical Image Analysis, 2022
9.CT-based Radiogenomic Analysis Dissects Intratumor Heterogeneity and Predicts Prognosis of Colorectal Cancer: A Multi-institutional Retrospective Study, Journal of Translational Medicine, 2022
10.A Novel Cell‐free DNA Methylation‐based Model Improves the Early Detection of Colorectal Cancer, Molecular Oncology, 2021
11.Predicting Treatment Response from Longitudinal Images Using Multi-task Deep Learning, Nature Communications, 2021
12.Genome-wide Discovery of a Novel Gene-expression Signature for the Identification of Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma, Annals of Surgery, 2019
13.Long-read RNA Sequencing Identifies Alternative Splice Variants in Hepatocellular Carcinoma and Tumor-specific Isoforms, Hepatology, 2019
14.DeepCC: A Novel Deep Learning-based Framework for Cancer Molecular Subtype Classification, Oncogenesis, 2019
15.A Genome-wide Transcriptomic Approach Identifies a Novel Gene Expression Signature for the Detection of Lymph Node Metastasis in Patients with Early-stage Gastric Cancer, EBioMedicine, 2019
16.RNAMethyPro: A Biologically Conserved Signature of N6-methyladenosine Regulators for Predicting Survival at Pan-cancer Level, NPJ Precision Oncology, 2019
17.Gene Expression Signature in Surgical Tissues and Endoscopic Biopsies Identifies High-risk T1 Colorectal Cancers, Gastroenterology, 2019
18.Genome-wide Discovery and Identification of a Novel miRNA Signature for Recurrence Prediction in Stage II and III Colorectal Cancer, Clinical Cancer Research, 2018
19.A Novel Non-Invasive Circulating miRNA Signature for Detection of Esophageal Adenocarcinoma, Gastroenterology, 2018