4 papers
DisMix: Order-Aware Mixup for Medical Imaging via Disentangling Ordinal and Non-Ordinal Features
Dileepa Pitawela, Gustavo Carneiro, Hsiang-Ting Chen
Image mixup is a widely adopted data augmentation strategy, yet it is ill-suited for ordinal classification tasks such as medical disease grading, where labels encode a progression…
Understanding Annotation Error Propagation and Learning an Adaptive Policy for Expert Intervention in Barrett's Video Segmentation
Lokesha Rasanjalee, Jin Lin Tan, Dileepa Pitawela +2
Accurate annotation of endoscopic videos is essential yet time-consuming, particularly for challenging datasets such as dysplasia in Barrett's esophagus, where the affected regions…
CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss
Dileepa Pitawela, Gustavo Carneiro, Hsiang-Ting Chen
In ordinal classification, misclassifying neighboring ranks is common, yet the consequences of these errors are not the same. For example, misclassifying benign tumor categories is…
Learning To Defer To A Population With Limited Demonstrations
Nilesh Ramgolam, Gustavo Carneiro, Hsiang-Ting Chen
This paper addresses the critical data scarcity that hinders the practical deployment of learning to defer (L2D) systems to the population. We introduce a context-aware, semi-super…