6 citations · 6 across the 5 of their papers we have counts for
5 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…
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…
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…
Toward a Human-Centered AI-assisted Colonoscopy System in Australia
Hsiang-Ting Chen, Yuan Zhang, Gustavo Carneiro +1
While AI-assisted colonoscopy promises improved colorectal cancer screening, its success relies on effective integration into clinical practice, not just algorithmic accuracy. This…
Deep Multimodal Learning with Missing Modality: A Survey
Renjie Wu, Hu Wang, Hsiang-Ting Chen +1
During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, negatively affectin…