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Yu Tian

Yu Tian is a postdoctoral research fellow at Harvard University. He received his Ph.D. in computer science at the Australian Institute for Machine Learning (AIML), University of Adelaide. Previously, Dr. Tian obtained his bachelor’s degree in computer science with First Class Honours at the University of Adelaide.


Research

My main research interests are in the fields of computer vision and medical image analysis, in particular trustworthy AI tasks including abnormality and rarity learning tasks, OoD detection, fairness learning, federated learning and their applications in medicine. I also have interests in developing multi-modal medical foundational models recently.

🔥🔥 I am actively seeking motivated graduate students and senior undergraduates interested in collaboration. I am also committed to mentorship and welcome inquiries from those new to the field of computer vision and medical AI. Please feel free to email me if you're interested. Remote collaboration is highly encouraged!

News

May, 2024 Two recent papers are accepted to Medical Image Analysis. One about artifact correction and another about malignant breast lesion detection.
Feb, 2024 One paper is accepted to IEEE Transactions on Medical Imaging (TMI).
Feb, 2024 Two papers are accepted to CVPR2024.
Feb, 2024 One paper on OoD detection for NLP systems is accepted in COLING2024.
Jan, 2024 Two papers are accepted in ICLR2024. One about fairness learning and another about zero-shot anomaly detection.
Aug, 2023 One paper about medical anomaly detection is accepted in MICCAI-MLMI.

Selected Publications

* denotes equal contribution; ^ denotes corresponding authorship.

  1. Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization
    Yan Luo*, Yu Tian*, Min Shi*, Tobias Elze, and Mengyu Wang
    IEEE Transactions on Medical Imaging (TMI), 2024
  2. FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling
    Yu Tian, Yan Luo, Min Shi, Ava Kouhana, Tobias Elze, and Mengyu Wang
    International Conference on Learning Representations (ICLR), 2024
  3. AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
    Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, and Jiming Chen
    International Conference on Learning Representations (ICLR), 2024
  4. Semantic Role Labeling Guided Out-of-distribution Detection
    Jinan Zou*, Maihao Guo*, Yu Tian*, Yuhao Lin, Haiyao Cao, Lingqiao Liu, Ehsan Abbasnejad, and Javen Qinfeng Shi
    International Conference on Computational Linguistics (COLING), 2024
  5. Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning
    Yan Luo*, Min Shi*, Yu Tian*, Tobias Elze, and Mengyu Wang
    In Proceedings of the IEEE/CVF international conference on computer vision (ICCV), 2023
  6. Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images
    Yu Tian*, Fengbei Liu*, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W Verjans, Rajvinder Singh, and Gustavo Carneiro
    Medical Image Analysis (MedIA), 2023
  7. Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
    Yu Tian*, Yuyuan Liu*, Guansong Pang, Fengbei Liu, Yuanhong Chen, and Gustavo Carneiro
    European Conference on Computer Vision (ECCV Oral), 2022
  8. Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection
    Yu Tian, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan W Verjans, and Gustavo Carneiro
    International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI Early Accept), 2022
  9. ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
    Fengbei Liu*, Yu Tian*, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, and Gustavo Carneiro
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
  10. Deep One-Class Classification via Interpolated Gaussian Descriptor
    Yuanhong Chen*, Yu Tian*^, Guansong Pang, and Gustavo Carneiro
    In Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI Oral), 2022
  11. Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
    Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W Verjans, and Gustavo Carneiro
    In Proceedings of the IEEE/CVF international conference on computer vision (ICCV), 2021
  12. Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
    Yu Tian, Guansong Pang, Fengbei Liu, Seon Ho Shin, Johan W Verjans, Rajvinder Singh, Gustavo Carneiro, and  others
    In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021
  13. Few-shot anomaly detection for polyp frames from colonoscopy
    Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W Verjans, and Gustavo Carneiro
    In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020