Probabilistic Domain Adaptation for Biomedical Image Segmentation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913970066030592 |
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| author | Archit, Anwai Pape, Constantin |
| author_facet | Archit, Anwai Pape, Constantin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_11790 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Probabilistic Domain Adaptation for Biomedical Image Segmentation Archit, Anwai Pape, Constantin Computer Vision and Pattern Recognition 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. |
| title | Probabilistic Domain Adaptation for Biomedical Image Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2303.11790 |