# Towards Continuous Domain adaptation for Healthcare Source: [Towards Continuous Domain adaptation for Healthcare]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:18:52.954Z Authors: Rahul Venkataramani, Hariharan Ravishankar, Saihareesh Anamandra Published: 2018-12-04T08:59:03.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()