Unpaired Modality Translation for Pseudo Labeling of Histology Images
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915047600553984 |
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| author | Boschet, Arthur Collin, Armand Katoch, Nishka Cohen-Adad, Julien |
| author_facet | Boschet, Arthur Collin, Armand Katoch, Nishka Cohen-Adad, Julien |
| contents | The segmentation of histological images is critical for various biomedical applications, yet the lack of annotated data presents a significant challenge. We propose a microscopy pseudo labeling pipeline utilizing unsupervised image translation to address this issue. Our method generates pseudo labels by translating between labeled and unlabeled domains without requiring prior annotation in the target domain. We evaluate two pseudo labeling strategies across three image domains increasingly dissimilar from the labeled data, demonstrating their effectiveness. Notably, our method achieves a mean Dice score of $0.736 \pm 0.005$ on a SEM dataset using the tutoring path, which involves training a segmentation model on synthetic data created by translating the labeled dataset (TEM) to the target modality (SEM). This approach aims to accelerate the annotation process by providing high-quality pseudo labels as a starting point for manual refinement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02858 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unpaired Modality Translation for Pseudo Labeling of Histology Images Boschet, Arthur Collin, Armand Katoch, Nishka Cohen-Adad, Julien Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition The segmentation of histological images is critical for various biomedical applications, yet the lack of annotated data presents a significant challenge. We propose a microscopy pseudo labeling pipeline utilizing unsupervised image translation to address this issue. Our method generates pseudo labels by translating between labeled and unlabeled domains without requiring prior annotation in the target domain. We evaluate two pseudo labeling strategies across three image domains increasingly dissimilar from the labeled data, demonstrating their effectiveness. Notably, our method achieves a mean Dice score of $0.736 \pm 0.005$ on a SEM dataset using the tutoring path, which involves training a segmentation model on synthetic data created by translating the labeled dataset (TEM) to the target modality (SEM). This approach aims to accelerate the annotation process by providing high-quality pseudo labels as a starting point for manual refinement. |
| title | Unpaired Modality Translation for Pseudo Labeling of Histology Images |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.02858 |