Pei Liu
I am an in-coming Postdoc research fellow at HNU, expected to start from Sept. 2025. I am fortunate to be advised by Prof. X. Zeng. Prior to that, I had a wonderful four-year journey at UESTC and got my Ph.D. degree in Jun. 2025 under supervision of Prof. L. Ji. I obtained my B.E./M.S. degree in computer science at UESTC in 2017/2020.
Research Interest: My research interests cover many aspects of machine learning and its applications in healthcare.
- Machine Learning: 1) Multiple Instance Learning (MIL), particularly the new issues posed by weak supervision; 2) Bayesian inference and uncertainty modeling, with more focus on NN-based approaches; and 3) Survival analysis, mainly in unbiased modeling for censored individuals.
- Computational Pathology (CPATH): utilizing efficient deep learning methods and explainable tools to unlock the potiential of Whole-Slide Image (WSI) for precise and personalized cancer diagnosis, prognosis, and treatment. I would like to devote myself to this direction for long-term and focus on cutting-edge research.
Publication & Activities: Most of my research papers have been published in interdisciplinary journals and computer science conferences, such as ICML, ICLR, AAAI, IEEE TMI, and MedIA. A full publication list can be found at google scholar or here. I also serve as reviewer in
- Conferences: ICML (2025), ICLR (2025), NeurIPS (2024, 2025), AAAI (2026), AISTATS (2025);
- Journals: IEEE TNNLS, IEEE TMI, TMLR, MedIA, IEEE JBHI, ESWA, Computers in Biology and Medicine.
More Things: N/A.
news
| Aug 7, 2025 | 📚 I am thrilled to announce our new work Path-PKT: Towards Understanding and Harnessing the Transferability of Prognostic Knowledge in Computational Pathology. This work presents the first systematic study on prognostic knowledge transfer in pathology towards understanding and harnessing the transferability of histological features for cancer prognosis. Paper interpretation can be found at Zhihu (中文). Its source code, UNI2-h-DSS dataset, and arXiv paper have been released. |
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| Jan 23, 2025 | 🎉 Our recent work, VLSA, (Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology), is accepted to ICLR 2025. You can check its code and paper. For the interpretation of VLSA, please visit Zhihu (中文). |
| Jan 9, 2025 |  One co-authored paper, WSOE (Weakly Supervised Outlier Exposure for Object-level Out-of-distribution detection), is accepted by Expert Systems with Applications. Congratulations to Yutian Lei. |
| Dec 10, 2024 |  Two co-authored papers, QPMIL-VL (Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification) and MVOL (Mining In-distribution Attributes in Outliers for Out-of-distribution Detection), are accepted at AAAI 2025. Congratulations to Yutian Lei and Jiaxiang Gou. For QPMIL-VL, please check its arXiv preprint & Code. For MVOL, please check its arXiv preprint & Code. |
| Nov 5, 2024 | 🎉 Received Top Reviewer Award as a PC member of NeurIPS 2024. |
| Aug 27, 2024 | 📚 I am excited to share that our two latest works, VLSA (Vision-Language-based Survival Analysis) and QPMIL-VL (Vision-Language-based Incremental Learning) for Computational Pathology. For VLSA, please check Preprint, Github, and Zhihu (中文). For QPMIL-VL, please check Preprint. Stay tuned for updates! |
| May 2, 2024 | I am thrilled to announce that MIREL, Weakly-supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation, is accepted at ICML 2024. Please check our arXiv paper, Github, and Poster. |
| Mar 31, 2024 | ProDiv, Prototype-driven Pseudo-bag Division for WSI Classification, is accepted by Computer Methods and Programs in Biomedicine. |
| Jan 3, 2024 | PseMix, Pseudo-bag Mixup Augmentation for WSI Classification, is accepted by IEEE Transaction on Medical Imaging. |
| Oct 31, 2023 | AdvMIL, Adversarial MIL for Survival Analysis on WSIs, is accepted by Medical Image Analysis. |
