Manish Nagireddy, Lamogha Chiazor, et al.
AAAI 2024
Deep learning algorithms require large amounts of labeled data which is difficult to attain for medical imaging. Even if a particular dataset is accessible, a learned classifier struggles to maintain the same level of performance on a different medical imaging dataset from a new or never-seen data source domain. Utilizing generative adversarial networks in a semi-supervised learning architecture, we address both problems of labeled data scarcity and data domain overfitting. For cardiac abnormality classification in chest X-rays, we demonstrate that an order of magnitude less data is required with semi-supervised learning generative adversarial networks than with conventional supervised learning convolutional neural networks. In addition, we demonstrate its robustness across different datasets for similar classification tasks.
Manish Nagireddy, Lamogha Chiazor, et al.
AAAI 2024
Zahra Ashktorab, Gagan Bansal, et al.
CHI 2024
Yan Liu, Xiaokang Chen, et al.
NeurIPS 2023
Alexander Timms, Abigail Langbridge, et al.
AAAI 2025