Shubh Goyal, Medha Hira, et al.
AAAI 2024
Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an active learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are then used within a novel class aware generative adversarial network (CAGAN) to generate realistic chest xray images for data augmentation by transferring characteristics from one class label to another. Experiments show our proposed AL framework is able to achieve state-of-the-art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.
Shubh Goyal, Medha Hira, et al.
AAAI 2024
Soumya Ghosh, Prasanna Sattigeri, et al.
NeurIPS 2023
Mathias Steiner
APS March Meeting 2024
Dina Bashkirova, Ben Usman, et al.
NeurIPS 2019