Self-supervised multi-task representation learning for sequential medical images
Self-supervised representation learning has achieved promising results for downstream visual tasks in natural images. However, its use in the medical domain, where there is an underlying anatomical structural similarity, remains underexplored. To address this shortcoming, we propose a self-supervise...
Autors principals: | Dong, N, Kampffmeyer, M, Voiculescu, I |
---|---|
Format: | Conference item |
Idioma: | English |
Publicat: |
Springer
2021
|
Ítems similars
-
Federated partially supervised learning with limited decentralized medical images
per: Dong, N, et al.
Publicat: (2022) -
Towards robust partially supervised multi-structure medical image segmentation on small-scale data
per: Dong, N, et al.
Publicat: (2021) -
Learning underrepresented classes from decentralized partially labeled medical images
per: Dong, N, et al.
Publicat: (2022) -
Multi-task self-supervised visual learning
per: Doersch, C, et al.
Publicat: (2017) -
Self-Supervised Learning for Invariant Representations From Multi-Spectral and SAR Images
per: Pallavi Jain, et al.
Publicat: (2022-01-01)