Towards Continuous Domain adaptation for Healthcare

• Main Authors: Venkataramani, Rahul, Ravishankar, Hariharan, Anamandra, Saihareesh
• Format: Journal Article
• Language: English
• Published: 04-12-2018
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning
• DOI: 10.48550/arxiv.1812.01281

Deep learning algorithms have demonstrated tremendous success on challenging
medical imaging problems. However, post-deployment, these algorithms are
susceptible to data distribution variations owing to \emph{limited data issues}
and \emph{diversity} in medical images. In this paper, we propose
\emph{ContextNets}, a generic memory-augmented neural network framework for
semantic segmentation to achieve continuous domain adaptation without the
necessity of retraining. Unlike existing methods which require access to entire
source and target domain images, our algorithm can adapt to a target domain
with a few similar images. We condition the inference on any new input with
features computed on its support set of images (and masks, if available)
through contextual embeddings to achieve site-specific adaptation. We
demonstrate state-of-the-art domain adaptation performance on the X-ray lung
segmentation problem from three independent cohorts that differ in disease
type, gender, contrast and intensity variations.