Probabilistic Domain Adaptation for Biomedical Image Segmentation

• URL: http://arxiv.org/abs/2303.11790v1
• Date: Tue, 21 Mar 2023 12:17:21 GMT
• Title: Probabilistic Domain Adaptation for Biomedical Image Segmentation
• Authors: Anwai Archit and Constantin Pape
• Abstract summary: We introduce a probabilistic domain adaptation method, building on self-training approaches and the Probabilistic UNet. We study joint and separate source-target training strategies and evaluate our method on three challenging domain adaptation tasks for biomedical segmentation.
• Score: 2.5382095320488665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmentation is a key analysis tasks in biomedical imaging. Given the many different experimental settings in this field, the lack of generalization limits the use of deep learning in practice. Domain adaptation is a promising remedy: it trains a model for a given task on a source dataset with labels and adapts it to a target dataset without additional labels. We introduce a probabilistic domain adaptation method, building on self-training approaches and the Probabilistic UNet. We use the latter to sample multiple segmentation hypothesis to implement better pseudo-label filtering. We further study joint and separate source-target training strategies and evaluate our method on three challenging domain adaptation tasks for biomedical segmentation.

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