FEW-SHOT image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitati...
| Päätekijät: | , , , , , , , , |
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| Aineistotyyppi: | Conference item |
| Kieli: | English |
| Julkaistu: |
IEEE
2022
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