Generative Active Learning with Variational Autoencoder for Radiology
  Data Generation in Veterinary Medicine
In-Gyu Lee Jun-Young Oh Hee-Jung Yu Jae-Hwan Kim Ki-Dong Eom Ji-Hoon Jeong
Recently, with increasing interest in pet healthcare, the demand for
computer-aided diagnosis (CAD) systems in veterinary medicine has increased.
The development of veterinary CAD has stagnated due to a lack of sufficient
radiology data. To overcome the challenge, we propose a generative active
learning framework based on a variational autoencoder. This approach aims to
alleviate the scarcity of reliable data for CAD systems in veterinary medicine.
This study utilizes datasets comprising cardiomegaly radiograph data. After
removing annotations and standardizing images, we employed a framework for data
augmentation, which consists of a data generation phase and a query phase for
filtering the generated data. The experimental results revealed that as the
data generated through this framework was added to the training data of the
generative model, the frechet inception distance consistently decreased from
84.14 to 50.75 on the radiograph. Subsequently, when the generated data were
incorporated into the training of the classification model, the false positive
of the confusion matrix also improved from 0.16 to 0.66 on the radiograph. The
proposed framework has the potential to address the challenges of data scarcity
in medical CAD, contributing to its advancement.