Programming and experiments by Junyang Cai '23
Abstract:
Convolutional neural networks (CNN) are a powerful deep learning method
for medical image segmentation. However they often lack generalizability
in clinical practice, as performance drops unhelpfully when models trained
from a particular source domain are transferred to a different target
domain (e.g. different vendor, acquisition parameters, protocols). To
address this issue, domain adaptation has attracted increasing attention
because it can minimize distribution differences among different but
related domains. Extending from this prior work, we introduce Co-Unet-GAN,
a co-learning domain adaptation and segmentation model addressing the
domain shift problem. In this model, we train a Unet segmentation network
and an image translation generative adversarial network (GAN) together to
generalize performance across domains given supervised data only in the
source domain. We evaluate our model on two large open echocardiography
datasets, using the CAMUS set as supervised source domain and
EchoNet-Dynamic as the unsupervised target. We obtain mean absolute error
on ejection fraction of 9.67% on Co-Unet-GAN compared to 11.28% for
a previously published Unet-GAN. Our Co-Unet-GAN for image translation
and segmentation is a promising solution to the domain shift problem.
Paper (pdf, preprint)
Poster (poster, SVURS)
Poster voiceover