16 citations · 41 across the 13 of their papers we have counts for
13 papers
Deep learning with noisy labels in medical prediction problems: a scoping review
Yishu Wei, Yu Deng, Cong Sun +3
Objectives: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adop…
Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learning
Mingquan Lin, Tianhao Li, Zhaoyi Sun +5
Purpose: Limited studies exploring concrete methods or approaches to tackle and enhance model fairness in the radiology domain. Our proposed AI model utilizes supervised contrastiv…
A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications
Gongbo Zhang, Yiliang Zhou, Yan Hu +3
Objectives Extraction of PICO (Populations, Interventions, Comparison, and Outcomes) entities is fundamental to evidence retrieval. We present a novel method PICOX to extract overl…
From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence
David Oniani, Jordan Hilsman, Yifan Peng +5
In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despi…
Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations
Mingquan Lin, Yuyun Xiao, Bojian Hou +6
In the United States, primary open-angle glaucoma (POAG) is the leading cause of blindness, especially among African American and Hispanic individuals. Deep learning has been widel…
Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances
Ajay Jaiswal, Tianlong Chen, Justin F. Rousseau +3
Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in im…