4 papers
SSTFB: Leveraging self-supervised pretext learning and temporal self-attention with feature branching for real-time video polyp segmentation
Ziang Xu, Jens Rittscher, Sharib Ali
Polyps are early cancer indicators, so assessing occurrences of polyps and their removal is critical. They are observed through a colonoscopy screening procedure that generates a s…
Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models
Willem Bonnaffé, CRUK ICGC Prostate Group, Freddie Hamdy +5
Recent advances in attention-based multiple instance learning (MIL) have improved our insights into the tissue regions that models rely on to make predictions in digital pathology.…
SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis
Ziang Xu, Jens Rittscher, Sharib Ali
Data-driven methods have shown tremendous progress in medical image analysis. In this context, deep learning-based supervised methods are widely popular. However, they require a la…
Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring
Ziang Xu, Sharib Ali, Soumya Gupta +3
Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is graded by endoscopists and this assessment is the basis for risk stratification and therapy monitoring.…