5 citations · 5 across the 2 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…
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…
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…
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…