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 abnormality and rarity learning tasks, such as image/video anomaly detection for surveillance and industrial applications or early detection of diseases.

News

Aug, 2023	One paper about medical anomaly detection is accepted in MICCAI-MLMI.
Jul, 2023	4 papers are accepted in ICCV 2023.
Jul, 2023	One paper about artifact correction is accepted in IEEE Journal of Biomedical and Health Informatics (JBHI).
Jul, 2023	One paper about medical anomaly detection is accepted in Medical Image Analysis.
Mar, 2023	Call for Participation for our CVPR 2023 tutorial on Recent Advances in Anomaly Detection.
Mar, 2023	One ARVO Imaging abstract is selected as oral presentation.

Selected Publications

* denotes equal contribution; ^ denotes corresponding authorship.

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

arXiv preprint 2023

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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

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