Conference item
Incremental learning of fetal heart anatomies using interpretable saliency maps
- Alternative title:
- Conference paper
- Abstract:
-
While medical image analysis has seen extensive use of deep neural networks, learning over multiple tasks is a challenge for connectionist networks due to tendencies of degradation in performance over old tasks while adapting to novel tasks. It is pertinent that adaptations to new data distributions over time are tractable with automated analysis methods as medical imaging data acquisition is typically not a static problem. So, one needs to ensure that a continual learning paradigm be ensured...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Accepted manuscript, 1.2MB)
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- Publisher copy:
- 10.1007/978-3-030-39343-4_11
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Authors
Bibliographic Details
- Publisher:
- Springer Publisher's website
- Journal:
- Communications in Computer and Information Science Journal website
- Volume:
- 1065
- Pages:
- 129-141
- Host title:
- MIUA 2019: Medical Image Understanding and Analysis
- Publication date:
- 2020-01-24
- Acceptance date:
- 2019-04-15
- Event title:
- MIUA 2019: Medical Image Understanding and Analysis
- Event location:
- Liverpool, UK
- Event website:
- https://miua2019.com/
- Event start date:
- 2019-07-24T00:00:00Z
- Event end date:
- 2019-07-26T00:00:00Z
- DOI:
- EISSN:
-
1865-0937
- ISSN:
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1865-0929
- ISBN:
- 9783030393427
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1087859
- Local pid:
- pubs:1087859
- Deposit date:
- 2020-05-14
Terms of use
- Copyright holder:
- Springer Nature Switzerland AG
- Copyright date:
- 2020
- Rights statement:
- © Springer Nature Switzerland AG 2020.
- Notes:
- This conference paper was presented at MIUA 2019: the 23rd Conference on Medical Image Understanding and Analysis, 24–26 July 2019, Liverpool, UK. This is the accepted manuscript version of the paper. The final version is available online from Springer at: https://doi.org/10.1007/978-3-030-39343-4_11
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