Probabilistic Domain Adaptation for Biomedical Image Segmentation
This work addresses the lack of generalization in biomedical imaging for segmentation, which is crucial for practical deep learning applications in this domain.
The paper tackled the problem of domain adaptation for biomedical image segmentation by introducing a probabilistic method that uses multiple segmentation hypotheses for better pseudo-label filtering, achieving state-of-the-art results on three challenging tasks with concrete performance gains.
Segmentation is a crucial analysis task in biomedical imaging. Given the diverse experimental settings in this field, the lack of generalization limits the use of deep learning in practice. Domain adaptation is a promising remedy: it involves training 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 hypotheses 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. Our code is publicly available at https://github.com/computational-cell-analytics/Probabilistic-Domain-Adaptation.