Conference item
Self-supervised contrastive video-speech representation learning for ultrasound
- Abstract:
-
In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a result, it would be beneficial if useful representations could be derived from raw data without the need for manual annotations. In this paper, we propose to address the problem of self-supervised representation learning with multi-modal ultrasound video-speech raw data. For this case, we assume that there is a high corr...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Bibliographic Details
- Publisher:
- Springer Publisher's website
- Host title:
- Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
- Series:
- Lecture Notes in Computer Science
- Journal:
- Lecture Notes in Computer Science Journal website
- Volume:
- 12263
- Pages:
- 534-543
- Publication date:
- 2020-09-29
- Event title:
- MICCAI 2020: Medical Image Computing and Computer Assisted Intervention
- Event location:
- Germany
- Event website:
- https://www.miccai2020.org/en/
- Event start date:
- 2020-10-04
- Event end date:
- 2020-10-08
- DOI:
- EISSN:
-
1611-3349
- ISSN:
-
0302-9743
- Pmid:
-
33103162
- ISBN:
- 9783030597153
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1139912
- Local pid:
- pubs:1139912
- Deposit date:
- 2020-11-11
Terms of use
- Copyright holder:
- Springer Nature
- Copyright date:
- 2020
- Rights statement:
- © Springer Nature Switzerland AG 2020
- Notes:
- This paper was presented at the MICCAI 2020: Medical Image Computing and Computer Assisted Intervention conference, 4th-8th October 2020, Lima, Peru. This is the accepted manuscript version of the article. The final version is available from Springer at: https://doi.org/10.1007/978-3-030-59716-0_51
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