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...
Huvudupphovsmän: | Dong, N, Kampffmeyer, M, Voiculescu, I |
---|---|
Materialtyp: | Conference item |
Språk: | English |
Publicerad: |
Springer
2021
|
Liknande verk
Liknande verk
-
Federated partially supervised learning with limited decentralized medical images
av: Dong, N, et al.
Publicerad: (2022) -
Towards robust partially supervised multi-structure medical image segmentation on small-scale data
av: Dong, N, et al.
Publicerad: (2021) -
Learning underrepresented classes from decentralized partially labeled medical images
av: Dong, N, et al.
Publicerad: (2022) -
Multi-task self-supervised visual learning
av: Doersch, C, et al.
Publicerad: (2017) -
Self-Supervised Learning for Invariant Representations From Multi-Spectral and SAR Images
av: Pallavi Jain, et al.
Publicerad: (2022-01-01)