23 citations · 96 across the 21 of their papers we have counts for
7 papers · 1 filter
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
Fengbei Liu, Yu Tian, Yuanhong Chen +3
Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and mult…
Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
Yuyuan Liu, Yu Tian, Yuanhong Chen +3
Consistency learning using input image, feature, or network perturbations has shown remarkable results in semi-supervised semantic segmentation, but this approach can be seriously…
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
Yu Tian, Yuyuan Liu, Guansong Pang +3
State-of-the-art (SOTA) anomaly segmentation approaches on complex urban driving scenes explore pixel-wise classification uncertainty learned from outlier exposure, or external rec…
Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images
Yu Tian, Fengbei Liu, Guansong Pang +5
Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images…
Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning
Yu Tian, Leonardo Zorron Cheng Tao Pu, Yuyuan Liu +6
In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos. The detection of frames with polyps is formulate…
Deep One-Class Classification via Interpolated Gaussian Descriptor
Yuanhong Chen, Yu Tian, Guansong Pang +1
One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data descript…
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
Yu Tian, Guansong Pang, Yuanhong Chen +3
Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing a…