1. Hui Liu, Yanfeng Wu, Tingting Xue, Zhe Du, Jun Xu, and Tao Jiang, Machine learning-assisted parametric analysis and multi-criteria optimization of building performance: A case study on indoor environmental quality and energy efficiency, Energy Conversion and Management, vol. 349, 120825, 2026.[Link to paper]

2. Mingxin Liu, Chengfei Cai, Depin Chen, Jun Li, Jinze Li, Wenlong Ming, and Jun Xu, Data- and Knowledge-Driven Multimodal Learning in Computational Pathology: A Comprehensive Survey, EngMedicine, 3(2), 100132, 2026.[Link to paper]

3. Junjie Zhu, Jin Li, Shen Zhao, Yiyan Deng, Yongming Miao, Jun Xu, Adapting LLMs for biomedical natural language processing: a comprehensive benchmark study on fine-tuning methods, The Journal of Supercomputing, vol.82, no.103, 2026. [Link to paper]

4. Qinhao Guo, Haoyu Cui, Yangyang Zhang, Shaoxian Tang, Weicheng Yan, Xiaoyan Zhou, Hongmei Ding, Jinhua Zhou, Xingzhu Ju, Zheng Feng, Jun Zhu, Fang Bai, Yanping Zhong, Haiming Li, Jun Xu, Xiaohua Wu, Xiangxue Wang, and Hao Wen, An Interpretable Deep Learning Model for Predicting Endometrial Cancer Molecular Subtypes from H&E-Stained Slides, npj Precision Oncology, 2026. [Link to paper]

5. Yuemei Luo, Yuan Li, Jiaxue Mei, Jun Xu, Linbo Liu, Xiaohua Qian, Automatic Identification of Retinopathy from Optical Coherence Tomography Images via a Similarity Matching Approach, 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE ICASSP 2026), 4-8 May 2026, Barcelona, Spain. [Link to paper]

6. Yuemei Luo, Yuan Li, Lei Tao, Jun Xu, and Linbo Liu, Dual-path Prompt-enhanced Semi-supervised Segmentation for Retinal Vessel Quantification in OCTA Imaging, Applied Soft Computing, 2026:114778. [Link to paper]

7. Yixuan Liu, Jiayi Ge, Geyang Xu, Chengfei Cai, Depin Chen, Jingru Tian, Jun Xu, Burden and Risk of Asthma and Rhinitis in Atopic Dermatitis: Global Estimates from a Hierarchical Bayesian Model, British Journal of Dermatology, vol. 194(6), p.1097–1106, 2026. [Link to paper]

8. Zheng Cao, Bang Du, Danqing Hu, Wei Zhou, Shangde Gao, Rong Tan, and Jun Xu, Few-shot Medical Image Segmentation with Hierarchical Hyper-correlation Vision State-space Model, IEEE Transactions on Computational Social Systems, 2026. [Link to paper]

9.Yuchen Zhao,Danyan Li,Teng Zhang,Xiangxue Wang, Jun Xu, and Kai Xuan, Hybrid Multi-View MRI Fusion for csPCa Diagnosis via Intra- and Inter-View Transformers, IEEE Journal of Biomedical and Health Informatics, 2026. [Link to paper]

10.Xiangxue Wang, Liya Chen, Jingwen Sun, Sirvan Khalighi, Tanmoy Dam, Himanshu Maurya, Tilak Pathak,  Cheng Lu,  Shipra Gandhi,  Sunil Badve,  Shen Zhao, Wentao Yang,  Jun Xu,  Anant Madabhushi, and Bolin Song, MuTriM: A Multiscale Deep Learning Model Integrating Longitudinal Radiomics and Pathomic Features for Predicting Recurrence and Adjuvant Radiation Benefit in Breast Cancer, European Journal of Cancer, Volume 0, Issue 0, 116679, 2026. [Link to paper]

11.Tianhao Zeng, Yilin He, Teng Zhang, Caiyue Ren, Jun Xu, Jingyi Cheng, Wenlong Ming, Dedicated breast PET-based deep learning radiomics for prediction of pathologic complete response to neoadjuvant chemotherapy in HER2-positive breast cancer, Cancers, 2026. [Link to paper]

12. Youran Wang, Xian Zeng, Lingjue Chen, Bin Li, Kaiqiang Shen, Jun Xu, Jie Sun, Early Prediction of Intraoperative Hypotension: Development and Validation of the HypoBridCast Hybrid Deep Learning Model, BMC Anesthesiology, 2026.[Link to paper]

13. Mingxin Liu, Chengfei Cai, Anwen Lu, Pengbo Xu, Jun Li, Jinze Li, Depin Chen, and Jun Xu, Synergistic Information Disentanglement for Omni-modal Slide Representation Learning in Computational Pathology, 29th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI),  2026.[Link to paper]

14. Wenlong Ming, Wenbin Ye, Mingxin Liu, Depin Chen, Yiping Jiao, Jun Xu and Xiangxue Wang, Noise-Aware Importance–Uncertainty Disentangled Multimodal Learning for Robust Cancer Survival Prediction, 29th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI),  2026.[Link to paper]

15. Hongying Yu, Bing Liu, Xian Zeng, Mucheng Ren, Zheng Cao, Xiaofeng Zhu, Xudong Lu, Jun Xu, Nan Wu, and Danqing Hu, Leveraging Large Language Models to Integrate Clinical Knowledge and Machine Learning Predictions for Lymph Node Metastasis Prediction: A Knowledge-Augmented Framework, JMIR Medical Informatics, 2026.[Link to paper]