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!
Selected Publications
* denotes equal contribution; ^ denotes corresponding authorship.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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