11 citations · 16 across the 3 of their papers we have counts for
5 papers
Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation
Yuanhong Chen, Hu Wang, Chong Wang +6
When analysing screening mammograms, radiologists can naturally process information across two ipsilateral views of each breast, namely the cranio-caudal (CC) and mediolateral-obli…
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…
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…
Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy
Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu +3
Anomaly detection methods generally target the learning of a normal image distribution (i.e., inliers showing healthy cases) and during testing, samples relatively far from the lea…
Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames
Yuyuan Liu, Yu Tian, Gabriel Maicas +4
The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such de…