4 papers
Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains
Samia Belhadj, Sanguk Park, Ambika Seth +2
Fairness in medical AI is increasingly recognized as a crucial aspect of healthcare delivery. While most of the prior work done on fairness emphasizes the importance of equal perfo…
SelectiveKD: A semi-supervised framework for cancer detection in DBT through Knowledge Distillation and Pseudo-labeling
Laurent Dillard, Hyeonsoo Lee, Weonsuk Lee +3
When developing Computer Aided Detection (CAD) systems for Digital Breast Tomosynthesis (DBT), the complexity arising from the volumetric nature of the modality poses significant t…
OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs
Gunhee Nam, Taesoo Kim, Sanghyup Lee +1
The accurate localization of inserted medical tubes and parts of human anatomy is a common problem when analyzing chest radiographs and something deep neural networks could potenti…
Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographs
Tae Soo Kim, Geonwoon Jang, Sanghyup Lee +1
As deep networks require large amounts of accurately labeled training data, a strategy to collect sufficiently large and accurate annotations is as important as innovations in reco…